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# catboost package installation
!pip install catboost
Requirement already satisfied: catboost in /usr/local/lib/python3.6/dist-packages (0.21) Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from catboost) (1.12.0) Requirement already satisfied: graphviz in /usr/local/lib/python3.6/dist-packages (from catboost) (0.10.1) Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from catboost) (1.17.5) Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from catboost) (3.1.3) Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from catboost) (1.4.1) Requirement already satisfied: plotly in /usr/local/lib/python3.6/dist-packages (from catboost) (4.4.1) Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.6/dist-packages (from catboost) (0.25.3) Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->catboost) (1.1.0) Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->catboost) (2.4.6) Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->catboost) (0.10.0) Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->catboost) (2.6.1) Requirement already satisfied: retrying>=1.3.3 in /usr/local/lib/python3.6/dist-packages (from plotly->catboost) (1.3.3) Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.24.0->catboost) (2018.9) Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from kiwisolver>=1.0.1->matplotlib->catboost) (45.1.0)
In [0]:
# importing libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import time
from datetime import datetime
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import train_test_split
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import RandomizedSearchCV
from sklearn.model_selection import GridSearchCV
import lightgbm as lgb
from catboost import CatBoostClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import GradientBoostingClassifier
from scipy import stats
import xgboost as xgb
# command for producing visualizations with higher resolution in notebook
plt.rcParams['figure.figsize'] = (13,8)
%config InlineBackend.figure_format = 'retina'
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# Connecting colab to drive to import data files.
from google.colab import drive
drive.mount('/content/gdrive')
Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount("/content/gdrive", force_remount=True).
Part 1: Customer Segmentation Report¶
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azdias = pd.read_csv('/content/gdrive/My Drive/Bertelsmann Data Science Project/Azdias.csv',dtype={18:'str',19:'str'})
customers = pd.read_csv('/content/gdrive/My Drive/Bertelsmann Data Science Project/Customers.csv',dtype={18:'str',19:'str'})
att_vals = pd.read_excel('/content/gdrive/My Drive/Bertelsmann Data Science Project/DIAS Attributes - Values 2017.xlsx')
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del att_vals['Unnamed: 0']
Data pre-processing¶
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# filling missing values of Unnamed: 1 with ffill.
att_vals['Unnamed: 1'].ffill(axis=0,inplace=True)
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att_vals.columns = att_vals.iloc[0].values
att_vals.drop(index = 0,inplace=True)
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features = att_vals['Attribute'].unique()
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# since we only have feature info of limited columns therefore we will be dropping columns whose description is not mentioned.
coll = [col for col in azdias.columns if col not in features]
azdias.drop(columns=coll,inplace=True)
customers.drop(columns=coll,inplace=True)
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# finding values using meaning column that represent missing values.
att_vals_na = att_vals[(att_vals['Meaning'].str.contains("unknown") | att_vals['Meaning'].str.contains("no "))]
pd.set_option('display.max_rows', 500)
att_vals_na
Out[0]:
| Attribute | Description | Value | Meaning | |
|---|---|---|---|---|
| 1 | AGER_TYP | best-ager typology | -1 | unknown |
| 2 | AGER_TYP | NaN | 0 | no classification possible |
| 6 | ALTERSKATEGORIE_GROB | age classification through prename analysis | -1, 0 | unknown |
| 12 | ALTER_HH | main age within the household | 0 | unknown / no main age detectable |
| 34 | ANREDE_KZ | gender | -1, 0 | unknown |
| 41 | BALLRAUM | distance to next urban centre | -1 | unknown |
| 49 | BIP_FLAG | business-flag indicating companies in the buil... | -1 | unknown |
| 50 | BIP_FLAG | NaN | 0 | no company in the building |
| 52 | CAMEO_DEUG_2015 | CAMEO classification 2015 - Uppergroup | -1 | unknown |
| 106 | CAMEO_DEUINTL_2015 | CAMEO classification 2015 - international typo... | -1 | unknown |
| 132 | CJT_GESAMTTYP | customer journey typology | 0 | unknown |
| 139 | D19_BANKEN_ANZ_12 | transaction activity BANKS in the last 12 months | 0 | no transactions known |
| 146 | D19_BANKEN_ANZ_24 | transaction activity BANKS in the last 24 months | 0 | no transactions known |
| 162 | D19_BANKEN_DATUM | NaN | 10 | no transactions known |
| 163 | D19_BANKEN_DIREKT_RZ | transactional activity based on the product gr... | 0 | no transaction known |
| 171 | D19_BANKEN_GROSS_RZ | transactional activity based on the product gr... | 0 | no transaction known |
| 179 | D19_BANKEN_LOKAL_RZ | transactional activity based on the product gr... | 0 | no transaction known |
| 196 | D19_BANKEN_OFFLINE_DATUM | NaN | 10 | no transactions known |
| 206 | D19_BANKEN_ONLINE_DATUM | NaN | 10 | no transactions known |
| 207 | D19_BANKEN_ONLINE_QUOTE_12 | amount of online transactions within all trans... | 0 | no Online-transactions within the last 12 months |
| 218 | D19_BANKEN_REST_RZ | transactional activity based on the product gr... | 0 | no transaction known |
| 226 | D19_BEKLEIDUNG_GEH_RZ | transactional activity based on the product gr... | 0 | no transaction known |
| 234 | D19_BEKLEIDUNG_REST_RZ | transactional activity based on the product gr... | 0 | no transaction known |
| 242 | D19_BILDUNG_RZ | transactional activity based on the product gr... | 0 | no transaction known |
| 250 | D19_BIO_OEKO_RZ | transactional activity based on the product gr... | 0 | no transaction known |
| 258 | D19_BUCH_RZ | transactional activity based on the product gr... | 0 | no transaction known |
| 266 | D19_DIGIT_SERV_RZ | transactional activity based on the product gr... | 0 | no transaction known |
| 274 | D19_DROGERIEARTIKEL_RZ | transactional activity based on the product gr... | 0 | no transaction known |
| 282 | D19_ENERGIE_RZ | transactional activity based on the product gr... | 0 | no transaction known |
| 290 | D19_FREIZEIT_RZ | transactional activity based on the product gr... | 0 | no transaction known |
| 298 | D19_GARTEN_RZ | transactional activity based on the product gr... | 0 | no transaction known |
| 306 | D19_GESAMT_ANZ_12 | transaction activity TOTAL POOL in the last 12... | 0 | no transactions known |
| 313 | D19_GESAMT_ANZ_24 | transaction activity TOTAL POOL in the last 24... | 0 | no transactions known |
| 329 | D19_GESAMT_DATUM | NaN | 10 | no transactions known |
| 339 | D19_GESAMT_OFFLINE_DATUM | NaN | 10 | no transactions known |
| 349 | D19_GESAMT_ONLINE_DATUM | NaN | 10 | no transactions known |
| 350 | D19_GESAMT_ONLINE_QUOTE_12 | amount of online transactions within all trans... | 0 | no Online-transactions within the last 12 months |
| 361 | D19_HANDWERK_RZ | transactional activity based on the product gr... | 0 | no transaction known |
| 369 | D19_HAUS_DEKO_RZ | transactional activity based on the product gr... | 0 | no transaction known |
| 377 | D19_KINDERARTIKEL_RZ | transactional activity based on the product gr... | 0 | no transaction known |
| 392 | D19_KK_KUNDENTYP | consumption movement in the last 12 months | -1 | unknown |
| 399 | D19_KOSMETIK_RZ | transactional activity based on the product gr... | 0 | no transaction known |
| 407 | D19_LEBENSMITTEL_RZ | transactional activity based on the product gr... | 0 | no transaction known |
| 415 | D19_LOTTO_RZ | transactional activity based on the product gr... | 0 | no transaction known |
| 423 | D19_NAHRUNGSERGAENZUNG_RZ | transactional activity based on the product gr... | 0 | no transaction known |
| 431 | D19_RATGEBER_RZ | transactional activity based on the product gr... | 0 | no transaction known |
| 439 | D19_REISEN_RZ | transactional activity based on the product gr... | 0 | no transaction known |
| 447 | D19_SAMMELARTIKEL_RZ | transactional activity based on the product gr... | 0 | no transaction known |
| 455 | D19_SCHUHE_RZ | transactional activity based on the product gr... | 0 | no transaction known |
| 463 | D19_SONSTIGE_RZ | transactional activity based on the product gr... | 0 | no transaction known |
| 471 | D19_TECHNIK_RZ | transactional activity based on the product gr... | 0 | no transaction known |
| 479 | D19_TELKO_ANZ_12 | transaction activity TELCO in the last 12 months | 0 | no transactions known |
| 486 | D19_TELKO_ANZ_24 | transaction activity TELCO in the last 24 months | 0 | no transactions known |
| 502 | D19_TELKO_DATUM | NaN | 10 | no transactions known |
| 503 | D19_TELKO_MOBILE_RZ | transactional activity based on the product gr... | 0 | no transaction known |
| 520 | D19_TELKO_OFFLINE_DATUM | NaN | 10 | no transactions known |
| 530 | D19_TELKO_ONLINE_DATUM | NaN | 10 | no transactions known |
| 531 | D19_TELKO_REST_RZ | transactional activity based on the product gr... | 0 | no transaction known |
| 539 | D19_TIERARTIKEL_RZ | transactional activity based on the product gr... | 0 | no transaction known |
| 547 | D19_VERSAND_ANZ_12 | transaction activity MAIL-ORDER in the last 12... | 0 | no transactions known |
| 554 | D19_VERSAND_ANZ_24 | transaction activity MAIL-ORDER in the last 24... | 0 | no transactions known |
| 570 | D19_VERSAND_DATUM | NaN | 10 | no transactions known |
| 580 | D19_VERSAND_OFFLINE_DATUM | NaN | 10 | no transactions known |
| 590 | D19_VERSAND_ONLINE_DATUM | NaN | 10 | no transactions known |
| 591 | D19_VERSAND_ONLINE_QUOTE_12 | amount of online transactions within all trans... | 0 | no Online-transactions within the last 12 months |
| 602 | D19_VERSAND_REST_RZ | transactional activity based on the product gr... | 0 | no transaction known |
| 610 | D19_VERSICHERUNGEN_RZ | transactional activity based on the product gr... | 0 | no transaction known |
| 618 | D19_VERSI_ANZ_12 | transaction activity INSURANCE in the last 12 ... | 0 | no transactions known |
| 625 | D19_VERSI_ANZ_24 | transaction activity INSURANCE in the last 24 ... | 0 | no transactions known |
| 632 | D19_VOLLSORTIMENT_RZ | transactional activity based on the product gr... | 0 | no transaction known |
| 640 | D19_WEIN_FEINKOST_RZ | transactional activity based on the product gr... | 0 | no transaction known |
| 648 | EWDICHTE | density of inhabitants per square kilometer | -1 | unknown |
| 655 | FINANZTYP | best descirbing financial type for the person | -1 | unknown |
| 662 | FINANZ_ANLEGER | financial typology: investor | -1 | unknown |
| 668 | FINANZ_HAUSBAUER | financial typology: main focus is the own house | -1 | unknown |
| 674 | FINANZ_MINIMALIST | financial typology: low financial interest | -1 | unknown |
| 680 | FINANZ_SPARER | financial typology: money saver | -1 | unknown |
| 686 | FINANZ_UNAUFFAELLIGER | financial typology: unremarkable | -1 | unknown |
| 692 | FINANZ_VORSORGER | financial typology: be prepared | -1 | unknown |
| 698 | GEBAEUDETYP | type of building (residential or commercial) | -1, 0 | unknown |
| 725 | GEOSCORE_KLS7 | microgeographical risk index concerning popula... | -1, 0 | unknown |
| 726 | GEOSCORE_KLS7 | NaN | 1 | almost no risk |
| 735 | HAUSHALTSSTRUKTUR | structure of the household (single-hh, couple ... | -1, 0 | unknown |
| 745 | HEALTH_TYP | health typology | -1 | unknown |
| 750 | HH_EINKOMMEN_SCORE | estimated household net income | -1, 0 | unknown |
| 757 | INNENSTADT | distance to the city centre | -1 | unknown |
| 766 | KBA05_ALTER1 | share of car owners less than 31 years old | -1, 9 | unknown |
| 772 | KBA05_ALTER2 | share of car owners inbetween 31 and 45 years ... | -1, 9 | unknown |
| 778 | KBA05_ALTER3 | share of car owners inbetween 45 and 60 years ... | -1, 9 | unknown |
| 784 | KBA05_ALTER4 | share of cars owners elder than 61 years | -1, 9 | unknown |
| 791 | KBA05_ANHANG | share of trailers in the microcell | -1, 9 | unknown |
| 796 | KBA05_ANTG1 | number of 1-2 family houses in the cell | -1 | unknown |
| 797 | KBA05_ANTG1 | NaN | 0 | no 1-2 family homes |
| 802 | KBA05_ANTG2 | number of 3-5 family houses in the cell | -1 | unknown |
| 803 | KBA05_ANTG2 | NaN | 0 | no 3-5 family homes |
| 808 | KBA05_ANTG3 | number of 6-10 family houses in the cell | -1 | unknown |
| 809 | KBA05_ANTG3 | NaN | 0 | no 6-10 family homes |
| 813 | KBA05_ANTG4 | number of >10 family houses in the cell | -1 | unknown |
| 814 | KBA05_ANTG4 | NaN | 0 | no >10 family homes |
| 822 | KBA05_AUTOQUOT | NaN | -1, 9 | unknown |
| 823 | KBA05_BAUMAX | most common building-type within the cell | -1, 0 | unknown |
| 829 | KBA05_CCM1 | share of cars with less than 1399ccm | -1, 9 | unknown |
| 835 | KBA05_CCM2 | share of cars with 1400ccm to 1799 ccm | -1, 9 | unknown |
| 841 | KBA05_CCM3 | share of cars with 1800ccm to 2499 ccm | -1, 9 | unknown |
| 847 | KBA05_CCM4 | share of cars with more than 2499ccm | -1, 9 | unknown |
| 853 | KBA05_DIESEL | share of cars with Diesel-engine in the microcell | -1, 9 | unknown |
| 859 | KBA05_FRAU | share of female car owners | -1, 9 | unknown |
| 865 | KBA05_GBZ | number of buildings in the microcell | -1, 0 | unknown |
| 871 | KBA05_HERST1 | share of top German manufacturer (Mercedes, BMW) | -1, 9 | unknown |
| 878 | KBA05_HERST2 | share of Volkswagen-Cars (including Audi) | -1, 9 | unknown |
| 885 | KBA05_HERST3 | share of Ford/Opel | -1, 9 | unknown |
| 892 | KBA05_HERST4 | share of European manufacturer (e.g. Fiat, Peu... | -1, 9 | unknown |
| 899 | KBA05_HERST5 | share of asian manufacturer (e.g. Toyota, Kia,... | -1, 9 | unknown |
| 906 | KBA05_HERSTTEMP | development of the most common car manufacture... | -1, 9 | unknown |
| 912 | KBA05_KRSAQUOT | share of cars per household (reffered to count... | -1, 9 | unknown |
| 918 | KBA05_KRSHERST1 | share of Mercedes/BMW (reffered to the county ... | -1, 9 | unknown |
| 924 | KBA05_KRSHERST2 | share of Volkswagen (reffered to the county av... | -1, 9 | unknown |
| 930 | KBA05_KRSHERST3 | share of Ford/Opel (reffered to the county ave... | -1, 9 | unknown |
| 936 | KBA05_KRSKLEIN | share of small cars (referred to the county av... | -1, 9 | unknown |
| 940 | KBA05_KRSOBER | share of upper class cars (referred to the cou... | -1, 9 | unknown |
| 944 | KBA05_KRSVAN | share of vans (referred to the county average) | -1, 9 | unknown |
| 948 | KBA05_KRSZUL | share of newbuilt cars (referred to the county... | -1, 9 | unknown |
| 952 | KBA05_KW1 | share of cars with less than 59 KW engine power | -1, 9 | unknown |
| 958 | KBA05_KW2 | share of cars with an engine power between 60 ... | -1, 9 | unknown |
| 964 | KBA05_KW3 | share of cars with an engine power of more tha... | -1, 9 | unknown |
| 970 | KBA05_MAXAH | most common age of car owners in the microcell | -1, 9 | unknown |
| 976 | KBA05_MAXBJ | most common age of the cars in the microcell | -1, 9 | unknown |
| 981 | KBA05_MAXHERST | most common car manufacturer in the microcell | -1, 9 | unknown |
| 987 | KBA05_MAXSEG | most common car segment in the microcell | -1, 9 | unknown |
| 992 | KBA05_MAXVORB | most common preowner structure in the microcell | -1, 9 | unknown |
| 993 | KBA05_MAXVORB | NaN | 1 | no preowner |
| 996 | KBA05_MOD1 | share of upper class cars (in an AZ specific d... | -1, 9 | unknown |
| 1002 | KBA05_MOD2 | share of middle class cars (in an AZ specific ... | -1, 9 | unknown |
| 1008 | KBA05_MOD3 | share of Golf-class cars (in an AZ specific de... | -1, 9 | unknown |
| 1014 | KBA05_MOD4 | share of small cars (in an AZ specific definit... | -1, 9 | unknown |
| 1021 | KBA05_MOD8 | share of vans (in an AZ specific definition) | -1, 9 | unknown |
| 1026 | KBA05_MODTEMP | development of the most common car segment in ... | -1, 9 | unknown |
| 1032 | KBA05_MOTOR | most common engine size in the microcell | -1, 9 | unknown |
| 1037 | KBA05_MOTRAD | share of motorcycles per household | -1, 9 | unknown |
| 1042 | KBA05_SEG1 | share of very small cars (Ford Ka etc.) in the... | -1, 9 | unknown |
| 1047 | KBA05_SEG10 | share of more specific cars (Vans, convertable... | -1, 9 | unknown |
| 1053 | KBA05_SEG2 | share of small and very small cars (Ford Fiest... | -1, 9 | unknown |
| 1059 | KBA05_SEG3 | share of lowe midclass cars (Ford Focus etc.) ... | -1, 9 | unknown |
| 1065 | KBA05_SEG4 | share of middle class cars (Ford Mondeo etc.) ... | -1, 9 | unknown |
| 1071 | KBA05_SEG5 | share of upper middle class cars and upper cla... | -1, 9 | unknown |
| 1077 | KBA05_SEG6 | share of upper class cars (BMW 7er etc.) in th... | -1, 9 | unknown |
| 1080 | KBA05_SEG7 | share of all-terrain vehicles and MUVs in the ... | -1, 9 | unknown |
| 1085 | KBA05_SEG8 | share of roadster and convertables in the micr... | -1, 9 | unknown |
| 1090 | KBA05_SEG9 | share of vans in the microcell | -1, 9 | unknown |
| 1095 | KBA05_VORB0 | share of cars with no preowner | -1, 9 | unknown |
| 1101 | KBA05_VORB1 | share of cars with one or two preowner | -1, 9 | unknown |
| 1107 | KBA05_VORB2 | share of cars with more than two preowner | -1, 9 | unknown |
| 1114 | KBA05_ZUL1 | share of cars built before 1994 | -1, 9 | unknown |
| 1120 | KBA05_ZUL2 | share of cars built between 1994 and 2000 | -1, 9 | unknown |
| 1126 | KBA05_ZUL3 | share of cars built between 2001 and 2002 | -1, 9 | unknown |
| 1133 | KBA05_ZUL4 | share of cars built from 2003 on | -1, 9 | unknown |
| 1140 | KBA13_ALTERHALTER_30 | share of car owners below 31 within the PLZ8 | -1 | unknown |
| 1147 | KBA13_ALTERHALTER_45 | share of car owners between 31 and 45 within t... | -1 | unknown |
| 1154 | KBA13_ALTERHALTER_60 | share of car owners between 46 and 60 within t... | -1 | unknown |
| 1161 | KBA13_ALTERHALTER_61 | share of car owners elder than 61 within the PLZ8 | -1 | unknown |
| 1169 | KBA13_AUDI | share of AUDI within the PLZ8 | -1 | unknown |
| 1176 | KBA13_AUTOQUOTE | share of cars per household within the PLZ8 | -1 | unknown |
| 1183 | KBA13_BJ_1999 | share of cars built between 1995 and 1999 with... | -1 | unknown |
| 1190 | KBA13_BJ_2000 | share of cars built between 2000 and 2003 with... | -1 | unknown |
| 1197 | KBA13_BJ_2004 | share of cars built before 2004 within the PLZ8 | -1 | unknown |
| 1204 | KBA13_BJ_2006 | share of cars built between 2005 and 2006 with... | -1 | unknown |
| 1211 | KBA13_BJ_2008 | share of cars built in 2008 within the PLZ8 | -1 | unknown |
| 1218 | KBA13_BJ_2009 | share of cars built in 2009 within the PLZ8 | -1 | unknown |
| 1225 | KBA13_BMW | share of BMW within the PLZ8 | -1 | unknown |
| 1232 | KBA13_CCM_1000 | share of cars with less than 1000ccm within th... | -1 | unknown |
| 1239 | KBA13_CCM_1200 | share of cars with 1000ccm to 1199ccm within t... | -1 | unknown |
| 1246 | KBA13_CCM_1400 | share of cars with 1200ccm to 1399ccm within t... | -1 | unknown |
| 1253 | KBA13_CCM_0_1400 | share of cars with less than 1400ccm within th... | -1 | unknown |
| 1260 | KBA13_CCM_1500 | share of cars with 1400ccm to 1499ccm within t... | -1 | unknown |
| 1267 | KBA13_CCM_1400_2500 | share of cars with 1400ccm to 2499ccm within t... | -1 | unknown |
| 1274 | KBA13_CCM_1600 | share of cars with 1500ccm to 1599ccm within t... | -1 | unknown |
| 1281 | KBA13_CCM_1800 | share of cars with 1600ccm to 1799ccm within t... | -1 | unknown |
| 1288 | KBA13_CCM_2000 | share of cars with 1800ccm to 1999ccm within t... | -1 | unknown |
| 1295 | KBA13_CCM_2500 | share of cars with 2000ccm to 2499ccm within t... | -1 | unknown |
| 1302 | KBA13_CCM_2501 | share of cars with more than 2500ccm within th... | -1 | unknown |
| 1309 | KBA13_CCM_3000 | share of cars with 2500ccm to 2999ccm within t... | -1 | unknown |
| 1316 | KBA13_CCM_3001 | share of cars with more than 3000ccm within th... | -1 | unknown |
| 1323 | KBA13_FAB_ASIEN | share of other Asian Manufacturers within the ... | -1 | unknown |
| 1330 | KBA13_FAB_SONSTIGE | share of other Manufacturers within the PLZ8 | -1 | unknown |
| 1337 | KBA13_FIAT | share of FIAT within the PLZ8 | -1 | unknown |
| 1344 | KBA13_FORD | share of FORD within the PLZ8 | -1 | unknown |
| 1351 | KBA13_HALTER_20 | share of car owners below 21 within the PLZ8 | -1 | unknown |
| 1358 | KBA13_HALTER_25 | share of car owners between 21 and 25 within t... | -1 | unknown |
| 1365 | KBA13_HALTER_30 | share of car owners between 26 and 30 within t... | -1 | unknown |
| 1372 | KBA13_HALTER_35 | share of car owners between 31 and 35 within t... | -1 | unknown |
| 1379 | KBA13_HALTER_40 | share of car owners between 36 and 40 within t... | -1 | unknown |
| 1386 | KBA13_HALTER_45 | share of car owners between 41 and 45 within t... | -1 | unknown |
| 1393 | KBA13_HALTER_50 | share of car owners between 46 and 50 within t... | -1 | unknown |
| 1400 | KBA13_HALTER_55 | share of car owners between 51 and 55 within t... | -1 | unknown |
| 1407 | KBA13_HALTER_60 | share of car owners between 56 and 60 within t... | -1 | unknown |
| 1414 | KBA13_HALTER_65 | share of car owners between 61 and 65 within t... | -1 | unknown |
| 1421 | KBA13_HALTER_66 | share of car owners over 66 within the PLZ8 | -1 | unknown |
| 1428 | KBA13_HERST_ASIEN | share of Asian Manufacturers within the PLZ8 | -1 | unknown |
| 1435 | KBA13_HERST_AUDI_VW | share of Volkswagen & Audi within the PLZ8 | -1 | unknown |
| 1442 | KBA13_HERST_BMW_BENZ | share of BMW & Mercedes Benz within the PLZ8 | -1 | unknown |
| 1449 | KBA13_HERST_EUROPA | share of European cars within the PLZ8 | -1 | unknown |
| 1456 | KBA13_HERST_FORD_OPEL | share of Ford & Opel/Vauxhall within the PLZ8 | -1 | unknown |
| 1463 | KBA13_HERST_SONST | share of other cars within the PLZ8 | -1 | unknown |
| 1470 | KBA13_KMH_110 | share of cars with max speed 110 km/h within t... | -1 | unknown |
| 1477 | KBA13_KMH_140 | share of cars with max speed between 110 km/h ... | -1 | unknown |
| 1484 | KBA13_KMH_180 | share of cars with max speed between 110 km/h ... | -1 | unknown |
| 1491 | KBA13_KMH_0_140 | share of cars with max speed 140 km/h within t... | -1 | unknown |
| 1498 | KBA13_KMH_140_210 | share of cars with max speed between 140 and 2... | -1 | unknown |
| 1505 | KBA13_KMH_211 | share of cars with a greater max speed than 21... | -1 | unknown |
| 1512 | KBA13_KMH_250 | share of cars with max speed between 210 and 2... | -1 | unknown |
| 1519 | KBA13_KMH_251 | share of cars with a greater max speed than 25... | -1 | unknown |
| 1526 | KBA13_KRSAQUOT | share of cars per household (referred to the c... | -1 | unknown |
| 1533 | KBA13_KRSHERST_AUDI_VW | share of Volkswagen (referred to the county av... | -1 | unknown |
| 1540 | KBA13_KRSHERST_BMW_BENZ | share of BMW/Mercedes Benz (referred to the co... | -1 | unknown |
| 1547 | KBA13_KRSHERST_FORD_OPEL | share of FORD/Opel (referred to the county ave... | -1 | unknown |
| 1554 | KBA13_KRSSEG_KLEIN | share of small cars (referred to the county av... | -1 | unknown |
| 1559 | KBA13_KRSSEG_OBER | share of upper class cars (referred to the cou... | -1 | unknown |
| 1564 | KBA13_KRSSEG_VAN | share of vans (referred to the county average)... | -1 | unknown |
| 1569 | KBA13_KRSZUL_NEU | share of newbuilt cars (referred to the county... | -1 | unknown |
| 1574 | KBA13_KW_30 | share of cars up to 30 KW engine power - PLZ8 | -1 | unknown |
| 1581 | KBA13_KW_40 | share of cars with an engine power between 31 ... | -1 | unknown |
| 1588 | KBA13_KW_50 | share of cars with an engine power between 41 ... | -1 | unknown |
| 1595 | KBA13_KW_60 | share of cars with an engine power between 51 ... | -1 | unknown |
| 1602 | KBA13_KW_0_60 | share of cars up to 60 KW engine power - PLZ8 | -1 | unknown |
| 1609 | KBA13_KW_70 | share of cars with an engine power between 61 ... | -1 | unknown |
| 1616 | KBA13_KW_61_120 | share of cars with an engine power between 61 ... | -1 | unknown |
| 1623 | KBA13_KW_80 | share of cars with an engine power between 71 ... | -1 | unknown |
| 1630 | KBA13_KW_90 | share of cars with an engine power between 81 ... | -1 | unknown |
| 1637 | KBA13_KW_110 | share of cars with an engine power between 91 ... | -1 | unknown |
| 1644 | KBA13_KW_120 | share of cars with an engine power between 111... | -1 | unknown |
| 1651 | KBA13_KW_121 | share of cars with an engine power more than 1... | -1 | unknown |
| 1658 | KBA13_MAZDA | share of MAZDA within the PLZ8 | -1 | unknown |
| 1665 | KBA13_MERCEDES | share of MERCEDES within the PLZ8 | -1 | unknown |
| 1672 | KBA13_MOTOR | most common motor size within the PLZ8 | -1 | unknown |
| 1678 | KBA13_NISSAN | share of NISSAN within the PLZ8 | -1 | unknown |
| 1685 | KBA13_OPEL | share of OPEL within the PLZ8 | -1 | unknown |
| 1692 | KBA13_PEUGEOT | share of PEUGEOT within the PLZ8 | -1 | unknown |
| 1699 | KBA13_RENAULT | share of RENAULT within the PLZ8 | -1 | unknown |
| 1706 | KBA13_SEG_GELAENDEWAGEN | share of allterrain within the PLZ8 | -1 | unknown |
| 1713 | KBA13_SEG_GROSSRAUMVANS | share of big sized vans within the PLZ8 | -1 | unknown |
| 1720 | KBA13_SEG_KLEINST | share of very small cars (Ford Ka etc.) in the... | -1 | unknown |
| 1727 | KBA13_SEG_KLEINWAGEN | share of small and very small cars (Ford Fiest... | -1 | unknown |
| 1734 | KBA13_SEG_KOMPAKTKLASSE | share of lowe midclass cars (Ford Focus etc.) ... | -1 | unknown |
| 1741 | KBA13_SEG_MINIVANS | share of minivans within the PLZ8 | -1 | unknown |
| 1748 | KBA13_SEG_MINIWAGEN | share of minicars within the PLZ8 | -1 | unknown |
| 1755 | KBA13_SEG_MITTELKLASSE | share of middle class cars (Ford Mondeo etc.) ... | -1 | unknown |
| 1762 | KBA13_SEG_OBEREMITTELKLASSE | share of upper middle class cars and upper cla... | -1 | unknown |
| 1769 | KBA13_SEG_OBERKLASSE | share of upper class cars (BMW 7er etc.) in th... | -1 | unknown |
| 1776 | KBA13_SEG_SONSTIGE | share of other cars within the PLZ8 | -1 | unknown |
| 1783 | KBA13_SEG_SPORTWAGEN | share of sportscars within the PLZ8 | -1 | unknown |
| 1790 | KBA13_SEG_UTILITIES | share of MUVs/SUVs within the PLZ8 | -1 | unknown |
| 1797 | KBA13_SEG_VAN | share of vans within the PLZ8 | -1 | unknown |
| 1804 | KBA13_SEG_WOHNMOBILE | share of roadmobiles within the PLZ8 | -1 | unknown |
| 1811 | KBA13_SITZE_4 | number of cars with less than 5 seats in the PLZ8 | -1 | unknown |
| 1818 | KBA13_SITZE_5 | number of cars with 5 seats in the PLZ8 | -1 | unknown |
| 1825 | KBA13_SITZE_6 | number of cars with more than 5 seats in the PLZ8 | -1 | unknown |
| 1832 | KBA13_TOYOTA | share of TOYOTA within the PLZ8 | -1 | unknown |
| 1839 | KBA13_VORB_0 | share of cars with no preowner - PLZ8 | -1 | unknown |
| 1846 | KBA13_VORB_1 | share of cars with 1 preowner - PLZ8 | -1 | unknown |
| 1853 | KBA13_VORB_1_2 | share of cars with 1 or 2 preowner - PLZ8 | -1 | unknown |
| 1860 | KBA13_VORB_2 | share of cars with 2 preowner - PLZ8 | -1 | unknown |
| 1867 | KBA13_VORB_3 | share of cars with 3 or more preowner - PLZ8 | -1 | unknown |
| 1874 | KBA13_VW | share of VOLKSWAGEN within the PLZ8 | -1 | unknown |
| 1881 | KKK | purchasing power | -1, 0 | unknown |
| 1994 | NATIONALITAET_KZ | nationaltity (scored by prename analysis) | -1, 0 | unknown |
| 2004 | ORTSGR_KLS9 | size of the community | -1 | unknown |
| 2014 | OST_WEST_KZ | flag indicating the former GDR/FRG | -1 | unknown |
| 2017 | PLZ8_ANTG1 | number of 1-2 family houses in the PLZ8 | -1 | unknown |
| 2023 | PLZ8_ANTG2 | number of 3-5 family houses in the PLZ8 | -1 | unknown |
| 2029 | PLZ8_ANTG3 | number of 6-10 family houses in the PLZ8 | -1 | unknown |
| 2034 | PLZ8_ANTG4 | number of >10 family houses in the PLZ8 | -1 | unknown |
| 2043 | PLZ8_GBZ | number of buildings within the PLZ8 | -1 | unknown |
| 2049 | PLZ8_HHZ | number of households within the PLZ8 | -1 | unknown |
| 2055 | PRAEGENDE_JUGENDJAHRE | dominating movement in the person's youth (ava... | -1, 0 | unknown |
| 2071 | REGIOTYP | neighbourhood | -1, 0 | unknown |
| 2084 | RELAT_AB | NaN | -1, 9 | unknown |
| 2085 | RETOURTYP_BK_S | return type | 0 | unknown |
| 2091 | SEMIO_DOM | affinity indicating in what way the person is ... | -1, 9 | unknown |
| 2099 | SEMIO_ERL | affinity indicating in what way the person is ... | -1, 9 | unknown |
| 2107 | SEMIO_FAM | affinity indicating in what way the person is ... | -1, 9 | unknown |
| 2115 | SEMIO_KAEM | affinity indicating in what way the person is ... | -1, 9 | unknown |
| 2123 | SEMIO_KRIT | affinity indicating in what way the person is ... | -1, 9 | unknown |
| 2131 | SEMIO_KULT | affinity indicating in what way the person is ... | -1, 9 | unknown |
| 2139 | SEMIO_LUST | affinity indicating in what way the person is ... | -1, 9 | unknown |
| 2147 | SEMIO_MAT | affinity indicating in what way the person is ... | -1, 9 | unknown |
| 2155 | SEMIO_PFLICHT | affinity indicating in what way the person is ... | -1, 9 | unknown |
| 2163 | SEMIO_RAT | affinity indicating in what way the person is ... | -1, 9 | unknown |
| 2171 | SEMIO_REL | affinity indicating in what way the person is ... | -1, 9 | unknown |
| 2179 | SEMIO_SOZ | affinity indicating in what way the person is ... | -1, 9 | unknown |
| 2187 | SEMIO_TRADV | affinity indicating in what way the person is ... | -1, 9 | unknown |
| 2195 | SEMIO_VERT | affinity indicating in what way the person is ... | -1, 9 | unknown |
| 2203 | SHOPPER_TYP | shopping typology | -1 | unknown |
| 2208 | SOHO_FLAG | small office/home office flag | -1 | unknown |
| 2209 | SOHO_FLAG | NaN | 0 | no small office/home office |
| 2211 | TITEL_KZ | flag whether this person holds an academic title | -1, 0 | unknown |
| 2217 | VERS_TYP | insurance typology | -1 | unknown |
| 2220 | WOHNDAUER_2008 | length of residence | -1, 0 | unknown |
| 2230 | WOHNLAGE | residential-area | -1 | unknown |
| 2231 | WOHNLAGE | NaN | 0 | no score calculated |
| 2239 | WACHSTUMSGEBIET_NB | growing area (population growth in the last 5 ... | -1, 0 | unknown |
| 2245 | W_KEIT_KIND_HH | likelihood of a child present in this household | -1, 0 | unknown |
| 2252 | ZABEOTYP | typification of energy consumers | -1, 9 | unknown |
In [0]:
nans = []
for attributes in att_vals_na['Attribute'].unique():
_ = att_vals_na.loc[att_vals_na['Attribute'] == attributes, 'Value'].astype(str).str.cat(sep=',')
_ = _.split(',')
nans.append(_)
In [0]:
nans = pd.concat([pd.Series(att_vals_na['Attribute'].unique()), pd.Series(nans)], axis=1)
nans.columns = ['Attributes', 'missing_or_unknown']
nans
Out[0]:
| Attributes | missing_or_unknown | |
|---|---|---|
| 0 | AGER_TYP | [-1, 0] |
| 1 | ALTERSKATEGORIE_GROB | [-1, 0] |
| 2 | ALTER_HH | [0] |
| 3 | ANREDE_KZ | [-1, 0] |
| 4 | BALLRAUM | [-1] |
| 5 | BIP_FLAG | [-1, 0] |
| 6 | CAMEO_DEUG_2015 | [-1] |
| 7 | CAMEO_DEUINTL_2015 | [-1] |
| 8 | CJT_GESAMTTYP | [0] |
| 9 | D19_BANKEN_ANZ_12 | [0] |
| 10 | D19_BANKEN_ANZ_24 | [0] |
| 11 | D19_BANKEN_DATUM | [10] |
| 12 | D19_BANKEN_DIREKT_RZ | [0] |
| 13 | D19_BANKEN_GROSS_RZ | [0] |
| 14 | D19_BANKEN_LOKAL_RZ | [0] |
| 15 | D19_BANKEN_OFFLINE_DATUM | [10] |
| 16 | D19_BANKEN_ONLINE_DATUM | [10] |
| 17 | D19_BANKEN_ONLINE_QUOTE_12 | [0] |
| 18 | D19_BANKEN_REST_RZ | [0] |
| 19 | D19_BEKLEIDUNG_GEH_RZ | [0] |
| 20 | D19_BEKLEIDUNG_REST_RZ | [0] |
| 21 | D19_BILDUNG_RZ | [0] |
| 22 | D19_BIO_OEKO_RZ | [0] |
| 23 | D19_BUCH_RZ | [0] |
| 24 | D19_DIGIT_SERV_RZ | [0] |
| 25 | D19_DROGERIEARTIKEL_RZ | [0] |
| 26 | D19_ENERGIE_RZ | [0] |
| 27 | D19_FREIZEIT_RZ | [0] |
| 28 | D19_GARTEN_RZ | [0] |
| 29 | D19_GESAMT_ANZ_12 | [0] |
| 30 | D19_GESAMT_ANZ_24 | [0] |
| 31 | D19_GESAMT_DATUM | [10] |
| 32 | D19_GESAMT_OFFLINE_DATUM | [10] |
| 33 | D19_GESAMT_ONLINE_DATUM | [10] |
| 34 | D19_GESAMT_ONLINE_QUOTE_12 | [0] |
| 35 | D19_HANDWERK_RZ | [0] |
| 36 | D19_HAUS_DEKO_RZ | [0] |
| 37 | D19_KINDERARTIKEL_RZ | [0] |
| 38 | D19_KK_KUNDENTYP | [-1] |
| 39 | D19_KOSMETIK_RZ | [0] |
| 40 | D19_LEBENSMITTEL_RZ | [0] |
| 41 | D19_LOTTO_RZ | [0] |
| 42 | D19_NAHRUNGSERGAENZUNG_RZ | [0] |
| 43 | D19_RATGEBER_RZ | [0] |
| 44 | D19_REISEN_RZ | [0] |
| 45 | D19_SAMMELARTIKEL_RZ | [0] |
| 46 | D19_SCHUHE_RZ | [0] |
| 47 | D19_SONSTIGE_RZ | [0] |
| 48 | D19_TECHNIK_RZ | [0] |
| 49 | D19_TELKO_ANZ_12 | [0] |
| 50 | D19_TELKO_ANZ_24 | [0] |
| 51 | D19_TELKO_DATUM | [10] |
| 52 | D19_TELKO_MOBILE_RZ | [0] |
| 53 | D19_TELKO_OFFLINE_DATUM | [10] |
| 54 | D19_TELKO_ONLINE_DATUM | [10] |
| 55 | D19_TELKO_REST_RZ | [0] |
| 56 | D19_TIERARTIKEL_RZ | [0] |
| 57 | D19_VERSAND_ANZ_12 | [0] |
| 58 | D19_VERSAND_ANZ_24 | [0] |
| 59 | D19_VERSAND_DATUM | [10] |
| 60 | D19_VERSAND_OFFLINE_DATUM | [10] |
| 61 | D19_VERSAND_ONLINE_DATUM | [10] |
| 62 | D19_VERSAND_ONLINE_QUOTE_12 | [0] |
| 63 | D19_VERSAND_REST_RZ | [0] |
| 64 | D19_VERSICHERUNGEN_RZ | [0] |
| 65 | D19_VERSI_ANZ_12 | [0] |
| 66 | D19_VERSI_ANZ_24 | [0] |
| 67 | D19_VOLLSORTIMENT_RZ | [0] |
| 68 | D19_WEIN_FEINKOST_RZ | [0] |
| 69 | EWDICHTE | [-1] |
| 70 | FINANZTYP | [-1] |
| 71 | FINANZ_ANLEGER | [-1] |
| 72 | FINANZ_HAUSBAUER | [-1] |
| 73 | FINANZ_MINIMALIST | [-1] |
| 74 | FINANZ_SPARER | [-1] |
| 75 | FINANZ_UNAUFFAELLIGER | [-1] |
| 76 | FINANZ_VORSORGER | [-1] |
| 77 | GEBAEUDETYP | [-1, 0] |
| 78 | GEOSCORE_KLS7 | [-1, 0, 1] |
| 79 | HAUSHALTSSTRUKTUR | [-1, 0] |
| 80 | HEALTH_TYP | [-1] |
| 81 | HH_EINKOMMEN_SCORE | [-1, 0] |
| 82 | INNENSTADT | [-1] |
| 83 | KBA05_ALTER1 | [-1, 9] |
| 84 | KBA05_ALTER2 | [-1, 9] |
| 85 | KBA05_ALTER3 | [-1, 9] |
| 86 | KBA05_ALTER4 | [-1, 9] |
| 87 | KBA05_ANHANG | [-1, 9] |
| 88 | KBA05_ANTG1 | [-1, 0] |
| 89 | KBA05_ANTG2 | [-1, 0] |
| 90 | KBA05_ANTG3 | [-1, 0] |
| 91 | KBA05_ANTG4 | [-1, 0] |
| 92 | KBA05_AUTOQUOT | [-1, 9] |
| 93 | KBA05_BAUMAX | [-1, 0] |
| 94 | KBA05_CCM1 | [-1, 9] |
| 95 | KBA05_CCM2 | [-1, 9] |
| 96 | KBA05_CCM3 | [-1, 9] |
| 97 | KBA05_CCM4 | [-1, 9] |
| 98 | KBA05_DIESEL | [-1, 9] |
| 99 | KBA05_FRAU | [-1, 9] |
| 100 | KBA05_GBZ | [-1, 0] |
| 101 | KBA05_HERST1 | [-1, 9] |
| 102 | KBA05_HERST2 | [-1, 9] |
| 103 | KBA05_HERST3 | [-1, 9] |
| 104 | KBA05_HERST4 | [-1, 9] |
| 105 | KBA05_HERST5 | [-1, 9] |
| 106 | KBA05_HERSTTEMP | [-1, 9] |
| 107 | KBA05_KRSAQUOT | [-1, 9] |
| 108 | KBA05_KRSHERST1 | [-1, 9] |
| 109 | KBA05_KRSHERST2 | [-1, 9] |
| 110 | KBA05_KRSHERST3 | [-1, 9] |
| 111 | KBA05_KRSKLEIN | [-1, 9] |
| 112 | KBA05_KRSOBER | [-1, 9] |
| 113 | KBA05_KRSVAN | [-1, 9] |
| 114 | KBA05_KRSZUL | [-1, 9] |
| 115 | KBA05_KW1 | [-1, 9] |
| 116 | KBA05_KW2 | [-1, 9] |
| 117 | KBA05_KW3 | [-1, 9] |
| 118 | KBA05_MAXAH | [-1, 9] |
| 119 | KBA05_MAXBJ | [-1, 9] |
| 120 | KBA05_MAXHERST | [-1, 9] |
| 121 | KBA05_MAXSEG | [-1, 9] |
| 122 | KBA05_MAXVORB | [-1, 9, 1] |
| 123 | KBA05_MOD1 | [-1, 9] |
| 124 | KBA05_MOD2 | [-1, 9] |
| 125 | KBA05_MOD3 | [-1, 9] |
| 126 | KBA05_MOD4 | [-1, 9] |
| 127 | KBA05_MOD8 | [-1, 9] |
| 128 | KBA05_MODTEMP | [-1, 9] |
| 129 | KBA05_MOTOR | [-1, 9] |
| 130 | KBA05_MOTRAD | [-1, 9] |
| 131 | KBA05_SEG1 | [-1, 9] |
| 132 | KBA05_SEG10 | [-1, 9] |
| 133 | KBA05_SEG2 | [-1, 9] |
| 134 | KBA05_SEG3 | [-1, 9] |
| 135 | KBA05_SEG4 | [-1, 9] |
| 136 | KBA05_SEG5 | [-1, 9] |
| 137 | KBA05_SEG6 | [-1, 9] |
| 138 | KBA05_SEG7 | [-1, 9] |
| 139 | KBA05_SEG8 | [-1, 9] |
| 140 | KBA05_SEG9 | [-1, 9] |
| 141 | KBA05_VORB0 | [-1, 9] |
| 142 | KBA05_VORB1 | [-1, 9] |
| 143 | KBA05_VORB2 | [-1, 9] |
| 144 | KBA05_ZUL1 | [-1, 9] |
| 145 | KBA05_ZUL2 | [-1, 9] |
| 146 | KBA05_ZUL3 | [-1, 9] |
| 147 | KBA05_ZUL4 | [-1, 9] |
| 148 | KBA13_ALTERHALTER_30 | [-1] |
| 149 | KBA13_ALTERHALTER_45 | [-1] |
| 150 | KBA13_ALTERHALTER_60 | [-1] |
| 151 | KBA13_ALTERHALTER_61 | [-1] |
| 152 | KBA13_AUDI | [-1] |
| 153 | KBA13_AUTOQUOTE | [-1] |
| 154 | KBA13_BJ_1999 | [-1] |
| 155 | KBA13_BJ_2000 | [-1] |
| 156 | KBA13_BJ_2004 | [-1] |
| 157 | KBA13_BJ_2006 | [-1] |
| 158 | KBA13_BJ_2008 | [-1] |
| 159 | KBA13_BJ_2009 | [-1] |
| 160 | KBA13_BMW | [-1] |
| 161 | KBA13_CCM_1000 | [-1] |
| 162 | KBA13_CCM_1200 | [-1] |
| 163 | KBA13_CCM_1400 | [-1] |
| 164 | KBA13_CCM_0_1400 | [-1] |
| 165 | KBA13_CCM_1500 | [-1] |
| 166 | KBA13_CCM_1400_2500 | [-1] |
| 167 | KBA13_CCM_1600 | [-1] |
| 168 | KBA13_CCM_1800 | [-1] |
| 169 | KBA13_CCM_2000 | [-1] |
| 170 | KBA13_CCM_2500 | [-1] |
| 171 | KBA13_CCM_2501 | [-1] |
| 172 | KBA13_CCM_3000 | [-1] |
| 173 | KBA13_CCM_3001 | [-1] |
| 174 | KBA13_FAB_ASIEN | [-1] |
| 175 | KBA13_FAB_SONSTIGE | [-1] |
| 176 | KBA13_FIAT | [-1] |
| 177 | KBA13_FORD | [-1] |
| 178 | KBA13_HALTER_20 | [-1] |
| 179 | KBA13_HALTER_25 | [-1] |
| 180 | KBA13_HALTER_30 | [-1] |
| 181 | KBA13_HALTER_35 | [-1] |
| 182 | KBA13_HALTER_40 | [-1] |
| 183 | KBA13_HALTER_45 | [-1] |
| 184 | KBA13_HALTER_50 | [-1] |
| 185 | KBA13_HALTER_55 | [-1] |
| 186 | KBA13_HALTER_60 | [-1] |
| 187 | KBA13_HALTER_65 | [-1] |
| 188 | KBA13_HALTER_66 | [-1] |
| 189 | KBA13_HERST_ASIEN | [-1] |
| 190 | KBA13_HERST_AUDI_VW | [-1] |
| 191 | KBA13_HERST_BMW_BENZ | [-1] |
| 192 | KBA13_HERST_EUROPA | [-1] |
| 193 | KBA13_HERST_FORD_OPEL | [-1] |
| 194 | KBA13_HERST_SONST | [-1] |
| 195 | KBA13_KMH_110 | [-1] |
| 196 | KBA13_KMH_140 | [-1] |
| 197 | KBA13_KMH_180 | [-1] |
| 198 | KBA13_KMH_0_140 | [-1] |
| 199 | KBA13_KMH_140_210 | [-1] |
| 200 | KBA13_KMH_211 | [-1] |
| 201 | KBA13_KMH_250 | [-1] |
| 202 | KBA13_KMH_251 | [-1] |
| 203 | KBA13_KRSAQUOT | [-1] |
| 204 | KBA13_KRSHERST_AUDI_VW | [-1] |
| 205 | KBA13_KRSHERST_BMW_BENZ | [-1] |
| 206 | KBA13_KRSHERST_FORD_OPEL | [-1] |
| 207 | KBA13_KRSSEG_KLEIN | [-1] |
| 208 | KBA13_KRSSEG_OBER | [-1] |
| 209 | KBA13_KRSSEG_VAN | [-1] |
| 210 | KBA13_KRSZUL_NEU | [-1] |
| 211 | KBA13_KW_30 | [-1] |
| 212 | KBA13_KW_40 | [-1] |
| 213 | KBA13_KW_50 | [-1] |
| 214 | KBA13_KW_60 | [-1] |
| 215 | KBA13_KW_0_60 | [-1] |
| 216 | KBA13_KW_70 | [-1] |
| 217 | KBA13_KW_61_120 | [-1] |
| 218 | KBA13_KW_80 | [-1] |
| 219 | KBA13_KW_90 | [-1] |
| 220 | KBA13_KW_110 | [-1] |
| 221 | KBA13_KW_120 | [-1] |
| 222 | KBA13_KW_121 | [-1] |
| 223 | KBA13_MAZDA | [-1] |
| 224 | KBA13_MERCEDES | [-1] |
| 225 | KBA13_MOTOR | [-1] |
| 226 | KBA13_NISSAN | [-1] |
| 227 | KBA13_OPEL | [-1] |
| 228 | KBA13_PEUGEOT | [-1] |
| 229 | KBA13_RENAULT | [-1] |
| 230 | KBA13_SEG_GELAENDEWAGEN | [-1] |
| 231 | KBA13_SEG_GROSSRAUMVANS | [-1] |
| 232 | KBA13_SEG_KLEINST | [-1] |
| 233 | KBA13_SEG_KLEINWAGEN | [-1] |
| 234 | KBA13_SEG_KOMPAKTKLASSE | [-1] |
| 235 | KBA13_SEG_MINIVANS | [-1] |
| 236 | KBA13_SEG_MINIWAGEN | [-1] |
| 237 | KBA13_SEG_MITTELKLASSE | [-1] |
| 238 | KBA13_SEG_OBEREMITTELKLASSE | [-1] |
| 239 | KBA13_SEG_OBERKLASSE | [-1] |
| 240 | KBA13_SEG_SONSTIGE | [-1] |
| 241 | KBA13_SEG_SPORTWAGEN | [-1] |
| 242 | KBA13_SEG_UTILITIES | [-1] |
| 243 | KBA13_SEG_VAN | [-1] |
| 244 | KBA13_SEG_WOHNMOBILE | [-1] |
| 245 | KBA13_SITZE_4 | [-1] |
| 246 | KBA13_SITZE_5 | [-1] |
| 247 | KBA13_SITZE_6 | [-1] |
| 248 | KBA13_TOYOTA | [-1] |
| 249 | KBA13_VORB_0 | [-1] |
| 250 | KBA13_VORB_1 | [-1] |
| 251 | KBA13_VORB_1_2 | [-1] |
| 252 | KBA13_VORB_2 | [-1] |
| 253 | KBA13_VORB_3 | [-1] |
| 254 | KBA13_VW | [-1] |
| 255 | KKK | [-1, 0] |
| 256 | NATIONALITAET_KZ | [-1, 0] |
| 257 | ORTSGR_KLS9 | [-1] |
| 258 | OST_WEST_KZ | [-1] |
| 259 | PLZ8_ANTG1 | [-1] |
| 260 | PLZ8_ANTG2 | [-1] |
| 261 | PLZ8_ANTG3 | [-1] |
| 262 | PLZ8_ANTG4 | [-1] |
| 263 | PLZ8_GBZ | [-1] |
| 264 | PLZ8_HHZ | [-1] |
| 265 | PRAEGENDE_JUGENDJAHRE | [-1, 0] |
| 266 | REGIOTYP | [-1, 0] |
| 267 | RELAT_AB | [-1, 9] |
| 268 | RETOURTYP_BK_S | [0] |
| 269 | SEMIO_DOM | [-1, 9] |
| 270 | SEMIO_ERL | [-1, 9] |
| 271 | SEMIO_FAM | [-1, 9] |
| 272 | SEMIO_KAEM | [-1, 9] |
| 273 | SEMIO_KRIT | [-1, 9] |
| 274 | SEMIO_KULT | [-1, 9] |
| 275 | SEMIO_LUST | [-1, 9] |
| 276 | SEMIO_MAT | [-1, 9] |
| 277 | SEMIO_PFLICHT | [-1, 9] |
| 278 | SEMIO_RAT | [-1, 9] |
| 279 | SEMIO_REL | [-1, 9] |
| 280 | SEMIO_SOZ | [-1, 9] |
| 281 | SEMIO_TRADV | [-1, 9] |
| 282 | SEMIO_VERT | [-1, 9] |
| 283 | SHOPPER_TYP | [-1] |
| 284 | SOHO_FLAG | [-1, 0] |
| 285 | TITEL_KZ | [-1, 0] |
| 286 | VERS_TYP | [-1] |
| 287 | WOHNDAUER_2008 | [-1, 0] |
| 288 | WOHNLAGE | [-1, 0] |
| 289 | WACHSTUMSGEBIET_NB | [-1, 0] |
| 290 | W_KEIT_KIND_HH | [-1, 0] |
| 291 | ZABEOTYP | [-1, 9] |
In [0]:
# re-encoding values that represent missing as np.NAN
for row in nans['Attributes']:
print(row)
if row in azdias.columns:
_m = nans.loc[nans['Attributes'] == row, 'missing_or_unknown'].iloc[0]
_i = azdias.loc[:, row].isin(_m)
azdias.loc[_i, row] = np.NaN
else:
continue
for row in nans['Attributes']:
if row in customers.columns:
_m = nans.loc[nans['Attributes'] == row, 'missing_or_unknown'].iloc[0]
_i = customers.loc[:, row].isin(_m)
customers.loc[_i, row] = np.NaN
else:
continue
AGER_TYP ALTERSKATEGORIE_GROB ALTER_HH ANREDE_KZ BALLRAUM BIP_FLAG CAMEO_DEUG_2015 CAMEO_DEUINTL_2015 CJT_GESAMTTYP D19_BANKEN_ANZ_12 D19_BANKEN_ANZ_24 D19_BANKEN_DATUM D19_BANKEN_DIREKT_RZ D19_BANKEN_GROSS_RZ D19_BANKEN_LOKAL_RZ D19_BANKEN_OFFLINE_DATUM D19_BANKEN_ONLINE_DATUM D19_BANKEN_ONLINE_QUOTE_12 D19_BANKEN_REST_RZ D19_BEKLEIDUNG_GEH_RZ D19_BEKLEIDUNG_REST_RZ D19_BILDUNG_RZ D19_BIO_OEKO_RZ D19_BUCH_RZ D19_DIGIT_SERV_RZ D19_DROGERIEARTIKEL_RZ D19_ENERGIE_RZ D19_FREIZEIT_RZ D19_GARTEN_RZ D19_GESAMT_ANZ_12 D19_GESAMT_ANZ_24 D19_GESAMT_DATUM D19_GESAMT_OFFLINE_DATUM D19_GESAMT_ONLINE_DATUM D19_GESAMT_ONLINE_QUOTE_12 D19_HANDWERK_RZ D19_HAUS_DEKO_RZ D19_KINDERARTIKEL_RZ D19_KK_KUNDENTYP D19_KOSMETIK_RZ D19_LEBENSMITTEL_RZ D19_LOTTO_RZ D19_NAHRUNGSERGAENZUNG_RZ D19_RATGEBER_RZ D19_REISEN_RZ D19_SAMMELARTIKEL_RZ D19_SCHUHE_RZ D19_SONSTIGE_RZ D19_TECHNIK_RZ D19_TELKO_ANZ_12 D19_TELKO_ANZ_24 D19_TELKO_DATUM D19_TELKO_MOBILE_RZ D19_TELKO_OFFLINE_DATUM D19_TELKO_ONLINE_DATUM D19_TELKO_REST_RZ D19_TIERARTIKEL_RZ D19_VERSAND_ANZ_12 D19_VERSAND_ANZ_24 D19_VERSAND_DATUM D19_VERSAND_OFFLINE_DATUM D19_VERSAND_ONLINE_DATUM D19_VERSAND_ONLINE_QUOTE_12 D19_VERSAND_REST_RZ D19_VERSICHERUNGEN_RZ D19_VERSI_ANZ_12 D19_VERSI_ANZ_24 D19_VOLLSORTIMENT_RZ D19_WEIN_FEINKOST_RZ EWDICHTE FINANZTYP FINANZ_ANLEGER FINANZ_HAUSBAUER FINANZ_MINIMALIST FINANZ_SPARER FINANZ_UNAUFFAELLIGER FINANZ_VORSORGER GEBAEUDETYP GEOSCORE_KLS7 HAUSHALTSSTRUKTUR HEALTH_TYP HH_EINKOMMEN_SCORE INNENSTADT KBA05_ALTER1 KBA05_ALTER2 KBA05_ALTER3 KBA05_ALTER4 KBA05_ANHANG KBA05_ANTG1 KBA05_ANTG2 KBA05_ANTG3 KBA05_ANTG4 KBA05_AUTOQUOT KBA05_BAUMAX KBA05_CCM1 KBA05_CCM2 KBA05_CCM3 KBA05_CCM4 KBA05_DIESEL KBA05_FRAU KBA05_GBZ KBA05_HERST1 KBA05_HERST2 KBA05_HERST3 KBA05_HERST4 KBA05_HERST5 KBA05_HERSTTEMP KBA05_KRSAQUOT KBA05_KRSHERST1 KBA05_KRSHERST2 KBA05_KRSHERST3 KBA05_KRSKLEIN KBA05_KRSOBER KBA05_KRSVAN KBA05_KRSZUL KBA05_KW1 KBA05_KW2 KBA05_KW3 KBA05_MAXAH KBA05_MAXBJ KBA05_MAXHERST KBA05_MAXSEG KBA05_MAXVORB KBA05_MOD1 KBA05_MOD2 KBA05_MOD3 KBA05_MOD4 KBA05_MOD8 KBA05_MODTEMP KBA05_MOTOR KBA05_MOTRAD KBA05_SEG1 KBA05_SEG10 KBA05_SEG2 KBA05_SEG3 KBA05_SEG4 KBA05_SEG5 KBA05_SEG6 KBA05_SEG7 KBA05_SEG8 KBA05_SEG9 KBA05_VORB0 KBA05_VORB1 KBA05_VORB2 KBA05_ZUL1 KBA05_ZUL2 KBA05_ZUL3 KBA05_ZUL4 KBA13_ALTERHALTER_30 KBA13_ALTERHALTER_45 KBA13_ALTERHALTER_60 KBA13_ALTERHALTER_61 KBA13_AUDI KBA13_AUTOQUOTE KBA13_BJ_1999 KBA13_BJ_2000 KBA13_BJ_2004 KBA13_BJ_2006 KBA13_BJ_2008 KBA13_BJ_2009 KBA13_BMW KBA13_CCM_1000 KBA13_CCM_1200 KBA13_CCM_1400 KBA13_CCM_0_1400 KBA13_CCM_1500 KBA13_CCM_1400_2500 KBA13_CCM_1600 KBA13_CCM_1800 KBA13_CCM_2000 KBA13_CCM_2500 KBA13_CCM_2501 KBA13_CCM_3000 KBA13_CCM_3001 KBA13_FAB_ASIEN KBA13_FAB_SONSTIGE KBA13_FIAT KBA13_FORD KBA13_HALTER_20 KBA13_HALTER_25 KBA13_HALTER_30 KBA13_HALTER_35 KBA13_HALTER_40 KBA13_HALTER_45 KBA13_HALTER_50 KBA13_HALTER_55 KBA13_HALTER_60 KBA13_HALTER_65 KBA13_HALTER_66 KBA13_HERST_ASIEN KBA13_HERST_AUDI_VW KBA13_HERST_BMW_BENZ KBA13_HERST_EUROPA KBA13_HERST_FORD_OPEL KBA13_HERST_SONST KBA13_KMH_110 KBA13_KMH_140 KBA13_KMH_180 KBA13_KMH_0_140 KBA13_KMH_140_210 KBA13_KMH_211 KBA13_KMH_250 KBA13_KMH_251 KBA13_KRSAQUOT KBA13_KRSHERST_AUDI_VW KBA13_KRSHERST_BMW_BENZ KBA13_KRSHERST_FORD_OPEL KBA13_KRSSEG_KLEIN KBA13_KRSSEG_OBER KBA13_KRSSEG_VAN KBA13_KRSZUL_NEU KBA13_KW_30 KBA13_KW_40 KBA13_KW_50 KBA13_KW_60 KBA13_KW_0_60 KBA13_KW_70 KBA13_KW_61_120 KBA13_KW_80 KBA13_KW_90 KBA13_KW_110 KBA13_KW_120 KBA13_KW_121 KBA13_MAZDA KBA13_MERCEDES KBA13_MOTOR KBA13_NISSAN KBA13_OPEL KBA13_PEUGEOT KBA13_RENAULT KBA13_SEG_GELAENDEWAGEN KBA13_SEG_GROSSRAUMVANS KBA13_SEG_KLEINST KBA13_SEG_KLEINWAGEN KBA13_SEG_KOMPAKTKLASSE KBA13_SEG_MINIVANS KBA13_SEG_MINIWAGEN KBA13_SEG_MITTELKLASSE KBA13_SEG_OBEREMITTELKLASSE KBA13_SEG_OBERKLASSE KBA13_SEG_SONSTIGE KBA13_SEG_SPORTWAGEN KBA13_SEG_UTILITIES KBA13_SEG_VAN KBA13_SEG_WOHNMOBILE KBA13_SITZE_4 KBA13_SITZE_5 KBA13_SITZE_6 KBA13_TOYOTA KBA13_VORB_0 KBA13_VORB_1 KBA13_VORB_1_2 KBA13_VORB_2 KBA13_VORB_3 KBA13_VW KKK NATIONALITAET_KZ ORTSGR_KLS9 OST_WEST_KZ PLZ8_ANTG1 PLZ8_ANTG2 PLZ8_ANTG3 PLZ8_ANTG4 PLZ8_GBZ PLZ8_HHZ PRAEGENDE_JUGENDJAHRE REGIOTYP RELAT_AB RETOURTYP_BK_S SEMIO_DOM SEMIO_ERL SEMIO_FAM SEMIO_KAEM SEMIO_KRIT SEMIO_KULT SEMIO_LUST SEMIO_MAT SEMIO_PFLICHT SEMIO_RAT SEMIO_REL SEMIO_SOZ SEMIO_TRADV SEMIO_VERT SHOPPER_TYP SOHO_FLAG TITEL_KZ VERS_TYP WOHNDAUER_2008 WOHNLAGE WACHSTUMSGEBIET_NB W_KEIT_KIND_HH ZABEOTYP
In [0]:
# Saving files to drive for further use
#%cd /content/gdrive/My Drive/Bertelsmann Data Science Project
#azdias = azdias.to_csv('azdias_with_missing')
#customers = customers.to_csv('customers_with_missing_values')
#nans = nans.to_csv('nans')
In [0]:
azdias.head()
Out[0]:
| AGER_TYP | ALTER_HH | ANZ_HAUSHALTE_AKTIV | ANZ_HH_TITEL | ANZ_PERSONEN | ANZ_TITEL | BALLRAUM | CAMEO_DEU_2015 | CAMEO_DEUG_2015 | CJT_GESAMTTYP | D19_BANKEN_ANZ_12 | D19_BANKEN_ANZ_24 | D19_BANKEN_DATUM | D19_BANKEN_OFFLINE_DATUM | D19_BANKEN_ONLINE_DATUM | D19_BANKEN_ONLINE_QUOTE_12 | D19_GESAMT_ANZ_12 | D19_GESAMT_ANZ_24 | D19_GESAMT_DATUM | D19_GESAMT_OFFLINE_DATUM | D19_GESAMT_ONLINE_DATUM | D19_GESAMT_ONLINE_QUOTE_12 | D19_KONSUMTYP | D19_TELKO_ANZ_12 | D19_TELKO_ANZ_24 | D19_TELKO_DATUM | D19_TELKO_OFFLINE_DATUM | D19_TELKO_ONLINE_DATUM | D19_VERSAND_ANZ_12 | D19_VERSAND_ANZ_24 | D19_VERSAND_DATUM | D19_VERSAND_OFFLINE_DATUM | D19_VERSAND_ONLINE_DATUM | D19_VERSAND_ONLINE_QUOTE_12 | D19_VERSI_ANZ_12 | D19_VERSI_ANZ_24 | EWDICHTE | FINANZ_ANLEGER | FINANZ_HAUSBAUER | FINANZ_MINIMALIST | ... | MIN_GEBAEUDEJAHR | MOBI_REGIO | NATIONALITAET_KZ | ONLINE_AFFINITAET | ORTSGR_KLS9 | OST_WEST_KZ | PLZ8_ANTG1 | PLZ8_ANTG2 | PLZ8_ANTG3 | PLZ8_ANTG4 | PLZ8_BAUMAX | PLZ8_GBZ | PLZ8_HHZ | PRAEGENDE_JUGENDJAHRE | REGIOTYP | RELAT_AB | RETOURTYP_BK_S | SEMIO_DOM | SEMIO_ERL | SEMIO_FAM | SEMIO_KAEM | SEMIO_KRIT | SEMIO_KULT | SEMIO_LUST | SEMIO_MAT | SEMIO_PFLICHT | SEMIO_RAT | SEMIO_REL | SEMIO_SOZ | SEMIO_TRADV | SEMIO_VERT | SHOPPER_TYP | TITEL_KZ | VERS_TYP | W_KEIT_KIND_HH | WOHNDAUER_2008 | WOHNLAGE | ZABEOTYP | ANREDE_KZ | ALTERSKATEGORIE_GROB | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 5.0 | 3.0 | 3.0 | ... | NaN | NaN | NaN | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 5.0 | 6.0 | 3.0 | 6.0 | 6.0 | 7.0 | 3.0 | 5.0 | 5.0 | 5.0 | 4.0 | 7.0 | 2.0 | 3.0 | 1.0 | NaN | NaN | NaN | NaN | NaN | NaN | 3.0 | 1.0 | 2.0 |
| 1 | NaN | NaN | 11.0 | 0.0 | 2.0 | 0.0 | 6.0 | 8A | 8.0 | 5.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 3.0 | 5.0 | 5.0 | 1.0 | ... | 1992.0 | 1.0 | 1.0 | 3.0 | 5.0 | W | 2.0 | 3.0 | 2.0 | 1.0 | 1.0 | 4.0 | 5.0 | 14.0 | 3.0 | 4.0 | 1.0 | 7.0 | 2.0 | 4.0 | 4.0 | 4.0 | 3.0 | 2.0 | 3.0 | 7.0 | 6.0 | 4.0 | 5.0 | 6.0 | 1.0 | 3.0 | NaN | 2.0 | 3.0 | 9.0 | 4.0 | 5.0 | 2.0 | 1.0 |
| 2 | NaN | 17.0 | 10.0 | 0.0 | 1.0 | 0.0 | 2.0 | 4C | 4.0 | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 9.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 4.0 | 2.0 | 5.0 | 1.0 | ... | 1992.0 | 3.0 | 1.0 | 2.0 | 5.0 | W | 3.0 | 3.0 | 1.0 | 0.0 | 1.0 | 4.0 | 4.0 | 15.0 | 2.0 | 2.0 | 3.0 | 7.0 | 6.0 | 1.0 | 7.0 | 7.0 | 3.0 | 4.0 | 3.0 | 3.0 | 4.0 | 3.0 | 4.0 | 3.0 | 4.0 | 2.0 | NaN | 1.0 | 3.0 | 9.0 | 2.0 | 5.0 | 2.0 | 3.0 |
| 3 | 2.0 | 13.0 | 1.0 | 0.0 | 0.0 | 0.0 | 4.0 | 2A | 2.0 | 2.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 9.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2.0 | 2.0 | 2.0 | 4.0 | ... | 1997.0 | 4.0 | 1.0 | 1.0 | 3.0 | W | 2.0 | 2.0 | 2.0 | 0.0 | 1.0 | 4.0 | 3.0 | 8.0 | NaN | 3.0 | 2.0 | 4.0 | 7.0 | 1.0 | 5.0 | 4.0 | 4.0 | 4.0 | 1.0 | 4.0 | 3.0 | 2.0 | 5.0 | 4.0 | 4.0 | 1.0 | NaN | 1.0 | NaN | 9.0 | 7.0 | 3.0 | 2.0 | 4.0 |
| 4 | NaN | 20.0 | 3.0 | 0.0 | 4.0 | 0.0 | 2.0 | 6B | 6.0 | 5.0 | 3.0 | 5.0 | 5.0 | NaN | 5.0 | 10.0 | 6.0 | 6.0 | 1.0 | 6.0 | 1.0 | 10.0 | 1.0 | NaN | 1.0 | 6.0 | 8.0 | NaN | 6.0 | 6.0 | 1.0 | 9.0 | 1.0 | 10.0 | 1.0 | 3.0 | 5.0 | 1.0 | 2.0 | 4.0 | ... | 1992.0 | 3.0 | 1.0 | 5.0 | 6.0 | W | 2.0 | 4.0 | 2.0 | 1.0 | 2.0 | 3.0 | 3.0 | 8.0 | 5.0 | 5.0 | 5.0 | 2.0 | 4.0 | 4.0 | 2.0 | 3.0 | 6.0 | 4.0 | 2.0 | 4.0 | 2.0 | 4.0 | 6.0 | 2.0 | 7.0 | 2.0 | NaN | 2.0 | 2.0 | 9.0 | 3.0 | 4.0 | 1.0 | 3.0 |
5 rows × 272 columns
In [0]:
azdias.describe()
Out[0]:
| AGER_TYP | ALTER_HH | ANZ_HAUSHALTE_AKTIV | ANZ_HH_TITEL | ANZ_PERSONEN | ANZ_TITEL | BALLRAUM | CJT_GESAMTTYP | D19_BANKEN_ANZ_12 | D19_BANKEN_ANZ_24 | D19_BANKEN_DATUM | D19_BANKEN_OFFLINE_DATUM | D19_BANKEN_ONLINE_DATUM | D19_BANKEN_ONLINE_QUOTE_12 | D19_GESAMT_ANZ_12 | D19_GESAMT_ANZ_24 | D19_GESAMT_DATUM | D19_GESAMT_OFFLINE_DATUM | D19_GESAMT_ONLINE_DATUM | D19_GESAMT_ONLINE_QUOTE_12 | D19_KONSUMTYP | D19_TELKO_ANZ_12 | D19_TELKO_ANZ_24 | D19_TELKO_DATUM | D19_TELKO_OFFLINE_DATUM | D19_TELKO_ONLINE_DATUM | D19_VERSAND_ANZ_12 | D19_VERSAND_ANZ_24 | D19_VERSAND_DATUM | D19_VERSAND_OFFLINE_DATUM | D19_VERSAND_ONLINE_DATUM | D19_VERSAND_ONLINE_QUOTE_12 | D19_VERSI_ANZ_12 | D19_VERSI_ANZ_24 | EWDICHTE | FINANZ_ANLEGER | FINANZ_HAUSBAUER | FINANZ_MINIMALIST | FINANZ_SPARER | FINANZ_UNAUFFAELLIGER | ... | LP_STATUS_GROB | MIN_GEBAEUDEJAHR | MOBI_REGIO | NATIONALITAET_KZ | ONLINE_AFFINITAET | ORTSGR_KLS9 | PLZ8_ANTG1 | PLZ8_ANTG2 | PLZ8_ANTG3 | PLZ8_ANTG4 | PLZ8_BAUMAX | PLZ8_GBZ | PLZ8_HHZ | PRAEGENDE_JUGENDJAHRE | REGIOTYP | RELAT_AB | RETOURTYP_BK_S | SEMIO_DOM | SEMIO_ERL | SEMIO_FAM | SEMIO_KAEM | SEMIO_KRIT | SEMIO_KULT | SEMIO_LUST | SEMIO_MAT | SEMIO_PFLICHT | SEMIO_RAT | SEMIO_REL | SEMIO_SOZ | SEMIO_TRADV | SEMIO_VERT | SHOPPER_TYP | TITEL_KZ | VERS_TYP | W_KEIT_KIND_HH | WOHNDAUER_2008 | WOHNLAGE | ZABEOTYP | ANREDE_KZ | ALTERSKATEGORIE_GROB | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 205378.000000 | 580954.000000 | 798073.000000 | 794213.000000 | 817722.000000 | 817722.000000 | 797481.000000 | 886367.000000 | 59487.000000 | 97121.000000 | 212890.000000 | 19686.000000 | 164239.000000 | 45234.000000 | 306424.000000 | 385918.000000 | 537051.000000 | 332663.000000 | 440226.000000 | 241033.000000 | 634108.000000 | 33231.000000 | 65013.000000 | 225423.000000 | 72107.000000 | 8203.000000 | 253249.000000 | 327403.000000 | 453335.000000 | 256988.000000 | 396757.000000 | 216741.000000 | 69932.000000 | 114184.000000 | 797481.000000 | 891221.000000 | 891221.000000 | 891221.000000 | 891221.000000 | 891221.000000 | ... | 886367.000000 | 798073.000000 | 757897.000000 | 782906.000000 | 886367.000000 | 794005.000000 | 774706.000000 | 774706.000000 | 774706.000000 | 774706.000000 | 774706.000000 | 774706.000000 | 774706.000000 | 783057.000000 | 733157.000000 | 793846.000000 | 886367.000000 | 891221.000000 | 891221.000000 | 891221.000000 | 891221.000000 | 891221.000000 | 891221.000000 | 891221.000000 | 891221.000000 | 891221.000000 | 891221.000000 | 891221.000000 | 891221.000000 | 891221.000000 | 891221.000000 | 780025.000000 | 2160.000000 | 780025.000000 | 743233.000000 | 817722.000000 | 791123.000000 | 891221.000000 | 891221.000000 | 891221.000000 |
| mean | 1.743410 | 15.291805 | 8.287263 | 0.040647 | 1.727637 | 0.004162 | 4.153043 | 3.632838 | 1.832804 | 2.017957 | 6.933200 | 6.685817 | 6.956204 | 9.886059 | 2.320027 | 2.864013 | 5.140640 | 7.413848 | 5.304019 | 9.368128 | 5.424540 | 1.315639 | 1.354437 | 7.741446 | 7.874617 | 8.020480 | 2.127839 | 2.610126 | 5.512548 | 7.665440 | 5.378252 | 9.492067 | 1.447692 | 1.615647 | 3.939172 | 3.033328 | 3.075121 | 3.074528 | 2.821039 | 2.874167 | ... | 2.432575 | 1993.277011 | 2.963540 | 1.168889 | 2.698691 | 5.293002 | 2.253330 | 2.801858 | 1.595426 | 0.699166 | 1.943913 | 3.381087 | 3.612821 | 9.280709 | 4.472086 | 3.071033 | 3.419630 | 4.667550 | 4.481405 | 4.272729 | 4.445007 | 4.763223 | 4.025014 | 4.359086 | 4.001597 | 4.256076 | 3.910139 | 4.240609 | 3.945860 | 3.661784 | 4.023709 | 1.590134 | 1.318519 | 1.511166 | 4.147141 | 7.908791 | 4.088440 | 3.362438 | 1.522098 | 2.777398 |
| std | 0.674312 | 3.800536 | 15.628087 | 0.324028 | 1.155849 | 0.068855 | 2.183710 | 1.595021 | 1.080954 | 1.226588 | 2.335306 | 2.421747 | 2.332829 | 0.765394 | 1.272181 | 1.497983 | 2.782871 | 2.047270 | 2.747413 | 1.640414 | 3.234275 | 0.632176 | 0.650309 | 1.825844 | 1.650424 | 1.558834 | 1.190232 | 1.434433 | 2.770594 | 1.863684 | 2.756699 | 1.497877 | 0.691940 | 0.834033 | 1.718996 | 1.529603 | 1.353248 | 1.321055 | 1.464749 | 1.486731 | ... | 1.474315 | 3.332739 | 1.428882 | 0.475075 | 1.521524 | 2.303739 | 0.972008 | 0.920309 | 0.986736 | 0.727137 | 1.459654 | 1.111598 | 0.973967 | 4.032107 | 1.836357 | 1.360532 | 1.417741 | 1.795712 | 1.807552 | 1.915885 | 1.852412 | 1.830789 | 1.903816 | 2.022829 | 1.857540 | 1.770137 | 1.580306 | 2.007373 | 1.946564 | 1.707637 | 2.077746 | 1.027972 | 0.999504 | 0.499876 | 1.784211 | 1.923137 | 1.920553 | 1.352704 | 0.499512 | 1.068775 |
| min | 1.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | ... | 1.000000 | 1985.000000 | 1.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 0.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 25% | 1.000000 | 13.000000 | 1.000000 | 0.000000 | 1.000000 | 0.000000 | 2.000000 | 2.000000 | 1.000000 | 1.000000 | 5.000000 | 5.000000 | 5.000000 | 10.000000 | 1.000000 | 2.000000 | 2.000000 | 6.000000 | 3.000000 | 10.000000 | 2.000000 | 1.000000 | 1.000000 | 7.000000 | 8.000000 | 8.000000 | 1.000000 | 1.000000 | 3.000000 | 7.000000 | 3.000000 | 10.000000 | 1.000000 | 1.000000 | 2.000000 | 2.000000 | 2.000000 | 2.000000 | 1.000000 | 2.000000 | ... | 1.000000 | 1992.000000 | 2.000000 | 1.000000 | 1.000000 | 4.000000 | 1.000000 | 2.000000 | 1.000000 | 0.000000 | 1.000000 | 3.000000 | 3.000000 | 6.000000 | 3.000000 | 2.000000 | 2.000000 | 3.000000 | 3.000000 | 3.000000 | 3.000000 | 3.000000 | 3.000000 | 2.000000 | 2.000000 | 3.000000 | 3.000000 | 3.000000 | 2.000000 | 2.000000 | 2.000000 | 1.000000 | 1.000000 | 1.000000 | 3.000000 | 8.000000 | 3.000000 | 3.000000 | 1.000000 | 2.000000 |
| 50% | 2.000000 | 16.000000 | 4.000000 | 0.000000 | 1.000000 | 0.000000 | 5.000000 | 4.000000 | 1.000000 | 2.000000 | 8.000000 | 8.000000 | 8.000000 | 10.000000 | 2.000000 | 3.000000 | 5.000000 | 8.000000 | 5.000000 | 10.000000 | 5.000000 | 1.000000 | 1.000000 | 9.000000 | 9.000000 | 9.000000 | 2.000000 | 2.000000 | 5.000000 | 8.000000 | 5.000000 | 10.000000 | 1.000000 | 1.000000 | 4.000000 | 3.000000 | 3.000000 | 3.000000 | 3.000000 | 3.000000 | ... | 2.000000 | 1992.000000 | 3.000000 | 1.000000 | 3.000000 | 5.000000 | 2.000000 | 3.000000 | 2.000000 | 1.000000 | 1.000000 | 3.000000 | 4.000000 | 9.000000 | 5.000000 | 3.000000 | 3.000000 | 5.000000 | 4.000000 | 4.000000 | 5.000000 | 5.000000 | 4.000000 | 5.000000 | 4.000000 | 4.000000 | 4.000000 | 4.000000 | 4.000000 | 3.000000 | 4.000000 | 2.000000 | 1.000000 | 2.000000 | 4.000000 | 9.000000 | 4.000000 | 3.000000 | 2.000000 | 3.000000 |
| 75% | 2.000000 | 18.000000 | 9.000000 | 0.000000 | 2.000000 | 0.000000 | 6.000000 | 5.000000 | 2.000000 | 3.000000 | 9.000000 | 9.000000 | 9.000000 | 10.000000 | 3.000000 | 4.000000 | 8.000000 | 9.000000 | 8.000000 | 10.000000 | 9.000000 | 2.000000 | 2.000000 | 9.000000 | 9.000000 | 9.000000 | 3.000000 | 4.000000 | 8.000000 | 9.000000 | 8.000000 | 10.000000 | 2.000000 | 2.000000 | 6.000000 | 5.000000 | 4.000000 | 4.000000 | 4.000000 | 4.000000 | ... | 4.000000 | 1993.000000 | 4.000000 | 1.000000 | 4.000000 | 7.000000 | 3.000000 | 3.000000 | 2.000000 | 1.000000 | 3.000000 | 4.000000 | 4.000000 | 14.000000 | 6.000000 | 4.000000 | 5.000000 | 6.000000 | 6.000000 | 6.000000 | 6.000000 | 6.000000 | 5.000000 | 6.000000 | 5.000000 | 6.000000 | 5.000000 | 6.000000 | 6.000000 | 5.000000 | 6.000000 | 2.000000 | 1.000000 | 2.000000 | 6.000000 | 9.000000 | 5.000000 | 4.000000 | 2.000000 | 4.000000 |
| max | 3.000000 | 21.000000 | 595.000000 | 23.000000 | 45.000000 | 6.000000 | 7.000000 | 6.000000 | 6.000000 | 6.000000 | 9.000000 | 9.000000 | 9.000000 | 10.000000 | 6.000000 | 6.000000 | 9.000000 | 9.000000 | 9.000000 | 10.000000 | 9.000000 | 6.000000 | 6.000000 | 9.000000 | 9.000000 | 9.000000 | 6.000000 | 6.000000 | 9.000000 | 9.000000 | 9.000000 | 10.000000 | 6.000000 | 6.000000 | 6.000000 | 5.000000 | 5.000000 | 5.000000 | 5.000000 | 5.000000 | ... | 5.000000 | 2016.000000 | 6.000000 | 3.000000 | 5.000000 | 9.000000 | 4.000000 | 4.000000 | 3.000000 | 2.000000 | 5.000000 | 5.000000 | 5.000000 | 15.000000 | 7.000000 | 5.000000 | 5.000000 | 7.000000 | 7.000000 | 7.000000 | 7.000000 | 7.000000 | 7.000000 | 7.000000 | 7.000000 | 7.000000 | 7.000000 | 7.000000 | 7.000000 | 7.000000 | 7.000000 | 3.000000 | 5.000000 | 2.000000 | 6.000000 | 9.000000 | 8.000000 | 6.000000 | 2.000000 | 9.000000 |
8 rows × 269 columns
In [0]:
customers.head()
Out[0]:
| AGER_TYP | ALTER_HH | ANZ_HAUSHALTE_AKTIV | ANZ_HH_TITEL | ANZ_PERSONEN | ANZ_TITEL | BALLRAUM | CAMEO_DEU_2015 | CAMEO_DEUG_2015 | CJT_GESAMTTYP | D19_BANKEN_ANZ_12 | D19_BANKEN_ANZ_24 | D19_BANKEN_DATUM | D19_BANKEN_OFFLINE_DATUM | D19_BANKEN_ONLINE_DATUM | D19_BANKEN_ONLINE_QUOTE_12 | D19_GESAMT_ANZ_12 | D19_GESAMT_ANZ_24 | D19_GESAMT_DATUM | D19_GESAMT_OFFLINE_DATUM | D19_GESAMT_ONLINE_DATUM | D19_GESAMT_ONLINE_QUOTE_12 | D19_KONSUMTYP | D19_TELKO_ANZ_12 | D19_TELKO_ANZ_24 | D19_TELKO_DATUM | D19_TELKO_OFFLINE_DATUM | D19_TELKO_ONLINE_DATUM | D19_VERSAND_ANZ_12 | D19_VERSAND_ANZ_24 | D19_VERSAND_DATUM | D19_VERSAND_OFFLINE_DATUM | D19_VERSAND_ONLINE_DATUM | D19_VERSAND_ONLINE_QUOTE_12 | D19_VERSI_ANZ_12 | D19_VERSI_ANZ_24 | EWDICHTE | FINANZ_ANLEGER | FINANZ_HAUSBAUER | FINANZ_MINIMALIST | ... | ONLINE_AFFINITAET | ORTSGR_KLS9 | OST_WEST_KZ | PLZ8_ANTG1 | PLZ8_ANTG2 | PLZ8_ANTG3 | PLZ8_ANTG4 | PLZ8_BAUMAX | PLZ8_GBZ | PLZ8_HHZ | PRAEGENDE_JUGENDJAHRE | REGIOTYP | RELAT_AB | RETOURTYP_BK_S | SEMIO_DOM | SEMIO_ERL | SEMIO_FAM | SEMIO_KAEM | SEMIO_KRIT | SEMIO_KULT | SEMIO_LUST | SEMIO_MAT | SEMIO_PFLICHT | SEMIO_RAT | SEMIO_REL | SEMIO_SOZ | SEMIO_TRADV | SEMIO_VERT | SHOPPER_TYP | TITEL_KZ | VERS_TYP | W_KEIT_KIND_HH | WOHNDAUER_2008 | WOHNLAGE | ZABEOTYP | PRODUCT_GROUP | CUSTOMER_GROUP | ONLINE_PURCHASE | ANREDE_KZ | ALTERSKATEGORIE_GROB | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2.0 | 10.0 | 1.0 | 0.0 | 2.0 | 0.0 | 3.0 | 1A | 1.0 | 5.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 9.0 | 9.0 | NaN | NaN | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 9.0 | 9.0 | NaN | NaN | NaN | NaN | 2.0 | 1.0 | 2.0 | 5.0 | ... | 3.0 | 2.0 | W | 3.0 | 3.0 | 1.0 | 0.0 | 1.0 | 5.0 | 5.0 | 4.0 | 1.0 | 1.0 | 5.0 | 1.0 | 3.0 | 5.0 | 1.0 | 3.0 | 4.0 | 7.0 | 6.0 | 2.0 | 1.0 | 2.0 | 6.0 | 1.0 | 6.0 | 3.0 | NaN | 1.0 | 6.0 | 9.0 | 7.0 | 3.0 | COSMETIC_AND_FOOD | MULTI_BUYER | 0 | 1.0 | 4.0 |
| 1 | NaN | 11.0 | NaN | NaN | 3.0 | 0.0 | NaN | NaN | NaN | NaN | NaN | 1.0 | 6.0 | NaN | NaN | NaN | NaN | 1.0 | 6.0 | NaN | 9.0 | NaN | 5.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 9.0 | NaN | 9.0 | NaN | NaN | NaN | NaN | 1.0 | 2.0 | 5.0 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 3.0 | 3.0 | 6.0 | 2.0 | 3.0 | 4.0 | 5.0 | 6.0 | 4.0 | 1.0 | 2.0 | 3.0 | 1.0 | 7.0 | 3.0 | NaN | 1.0 | NaN | 9.0 | NaN | 3.0 | FOOD | SINGLE_BUYER | 0 | 1.0 | 4.0 |
| 2 | NaN | 6.0 | 1.0 | 0.0 | 1.0 | 0.0 | 7.0 | 5D | 5.0 | 2.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 4.0 | 1.0 | 4.0 | 5.0 | ... | 1.0 | 5.0 | W | 2.0 | 3.0 | 3.0 | 1.0 | 3.0 | 2.0 | 3.0 | 4.0 | 7.0 | 3.0 | 5.0 | 5.0 | 7.0 | 2.0 | 6.0 | 7.0 | 1.0 | 7.0 | 3.0 | 4.0 | 2.0 | 1.0 | 2.0 | 1.0 | 3.0 | 1.0 | NaN | 2.0 | 6.0 | 9.0 | 2.0 | 3.0 | COSMETIC_AND_FOOD | MULTI_BUYER | 0 | 2.0 | 4.0 |
| 3 | 1.0 | 8.0 | 0.0 | NaN | 0.0 | 0.0 | 7.0 | 4C | 4.0 | 2.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 6.0 | 6.0 | NaN | NaN | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 | 6.0 | 6.0 | NaN | NaN | NaN | NaN | 1.0 | 2.0 | 2.0 | 5.0 | ... | 2.0 | 3.0 | W | 3.0 | 2.0 | 1.0 | 0.0 | 1.0 | 4.0 | 3.0 | 1.0 | 6.0 | 1.0 | 3.0 | 3.0 | 3.0 | 5.0 | 3.0 | 3.0 | 4.0 | 5.0 | 4.0 | 3.0 | 3.0 | 3.0 | 6.0 | 4.0 | 7.0 | 0.0 | NaN | 1.0 | NaN | 9.0 | 7.0 | 1.0 | COSMETIC | MULTI_BUYER | 0 | 1.0 | 4.0 |
| 4 | NaN | 20.0 | 7.0 | 0.0 | 4.0 | 0.0 | 3.0 | 7B | 7.0 | 6.0 | 1.0 | 2.0 | 3.0 | NaN | 7.0 | NaN | 3.0 | 5.0 | 1.0 | 8.0 | 1.0 | 10.0 | 1.0 | NaN | 1.0 | 7.0 | NaN | 9.0 | 3.0 | 5.0 | 1.0 | 8.0 | 1.0 | 10.0 | NaN | NaN | 4.0 | 4.0 | 2.0 | 3.0 | ... | 5.0 | 5.0 | W | 2.0 | 4.0 | 2.0 | 1.0 | 2.0 | 3.0 | 3.0 | 8.0 | 7.0 | 1.0 | 5.0 | 5.0 | 4.0 | 5.0 | 2.0 | 3.0 | 5.0 | 6.0 | 6.0 | 5.0 | 5.0 | 4.0 | 4.0 | 4.0 | 5.0 | 1.0 | NaN | 2.0 | 2.0 | 9.0 | 3.0 | 1.0 | FOOD | MULTI_BUYER | 0 | 1.0 | 3.0 |
5 rows × 275 columns
In [0]:
azdias.shape,customers.shape
Out[0]:
((891221, 272), (191652, 275))
In [0]:
# dropping the 3 extra columns in customers
coll = [col for col in customers.columns if col not in azdias.columns]
customers.drop(columns=coll,inplace=True)
In [0]:
azdias.shape,customers.shape
Out[0]:
((891221, 272), (191652, 272))
In [0]:
# converting all integer values to float values for consistency.
def fltconv(df):
for col in df.columns:
if df[col].dtype == np.int64:
df[col] = df[col].astype(np.float64)
In [0]:
fltconv(azdias)
fltconv(customers)
In [0]:
nan_in_cols = azdias.shape[0] - azdias.count()
In [0]:
plt.title('Distribution of missing values across population columns',fontdict = {'fontsize' : 20})
nan_in_cols.hist(bins=50);
In [0]:
# dropping columns with more than 200000 missing values in azdias.
drop_cols = azdias.columns[nan_in_cols > 200000]
print(drop_cols)
azdias.drop(drop_cols, axis=1, inplace = True)
azdias.shape
Index(['AGER_TYP', 'ALTER_HH', 'D19_BANKEN_ANZ_12', 'D19_BANKEN_ANZ_24',
'D19_BANKEN_DATUM', 'D19_BANKEN_OFFLINE_DATUM',
'D19_BANKEN_ONLINE_DATUM', 'D19_BANKEN_ONLINE_QUOTE_12',
'D19_GESAMT_ANZ_12', 'D19_GESAMT_ANZ_24', 'D19_GESAMT_DATUM',
'D19_GESAMT_OFFLINE_DATUM', 'D19_GESAMT_ONLINE_DATUM',
'D19_GESAMT_ONLINE_QUOTE_12', 'D19_KONSUMTYP', 'D19_TELKO_ANZ_12',
'D19_TELKO_ANZ_24', 'D19_TELKO_DATUM', 'D19_TELKO_OFFLINE_DATUM',
'D19_TELKO_ONLINE_DATUM', 'D19_VERSAND_ANZ_12', 'D19_VERSAND_ANZ_24',
'D19_VERSAND_DATUM', 'D19_VERSAND_OFFLINE_DATUM',
'D19_VERSAND_ONLINE_DATUM', 'D19_VERSAND_ONLINE_QUOTE_12',
'D19_VERSI_ANZ_12', 'D19_VERSI_ANZ_24', 'KBA05_ANTG1', 'KBA05_ANTG2',
'KBA05_ANTG3', 'KBA05_ANTG4', 'KBA05_BAUMAX', 'KBA05_MAXVORB',
'TITEL_KZ'],
dtype='object')
Out[0]:
(891221, 237)
In [0]:
nan_in_cols2 = customers.shape[0] - customers.count()
In [0]:
plt.title('Distribution of missing values across customers columns',fontdict = {'fontsize' : 20})
nan_in_cols2.hist(bins=50);
In [0]:
# dropping columns with more than 65000 missing values in customers.
drop_cols2 = customers.columns[nan_in_cols2 > 65000]
print(drop_cols2)
customers.drop(drop_cols, axis=1, inplace = True)
customers.shape
Index(['AGER_TYP', 'ALTER_HH', 'D19_BANKEN_ANZ_12', 'D19_BANKEN_ANZ_24',
'D19_BANKEN_DATUM', 'D19_BANKEN_OFFLINE_DATUM',
'D19_BANKEN_ONLINE_DATUM', 'D19_BANKEN_ONLINE_QUOTE_12',
'D19_GESAMT_ANZ_12', 'D19_GESAMT_ANZ_24', 'D19_GESAMT_OFFLINE_DATUM',
'D19_GESAMT_ONLINE_DATUM', 'D19_GESAMT_ONLINE_QUOTE_12',
'D19_TELKO_ANZ_12', 'D19_TELKO_ANZ_24', 'D19_TELKO_DATUM',
'D19_TELKO_OFFLINE_DATUM', 'D19_TELKO_ONLINE_DATUM',
'D19_VERSAND_ANZ_12', 'D19_VERSAND_ANZ_24', 'D19_VERSAND_DATUM',
'D19_VERSAND_OFFLINE_DATUM', 'D19_VERSAND_ONLINE_DATUM',
'D19_VERSAND_ONLINE_QUOTE_12', 'D19_VERSI_ANZ_12', 'D19_VERSI_ANZ_24',
'KBA05_ANTG1', 'KBA05_ANTG2', 'KBA05_ANTG3', 'KBA05_ANTG4',
'KBA05_BAUMAX', 'KBA05_MAXVORB', 'TITEL_KZ'],
dtype='object')
Out[0]:
(191652, 237)
In [0]:
nan_in_rows = azdias.shape[1] - azdias.count(axis = 1)
In [0]:
plt.title('Distribution of missing values across population rows',fontdict = {'fontsize' : 20})
nan_in_rows.hist(bins=20);
In [0]:
# dropping rows with more than 50 missing values in azdias.
drop_rows = azdias.index[nan_in_rows > 50]
print(drop_rows)
azdias.drop(drop_rows, axis=0, inplace = True)
azdias.shape
Int64Index([ 0, 11, 12, 13, 14, 15, 17, 20,
23, 24,
...
891164, 891169, 891170, 891171, 891172, 891173, 891175, 891185,
891187, 891203],
dtype='int64', length=153933)
Out[0]:
(737288, 237)
In [0]:
nan_in_rows2 = customers.shape[1] - customers.count(axis = 1)
In [0]:
plt.title('Distribution of missing values across customer rows',fontdict = {'fontsize' : 20})
nan_in_rows2.hist(bins=20);
In [0]:
# dropping rows with more than 50 missing values in customers.
drop_rows2 = customers.index[nan_in_rows2 > 50]
print(drop_rows2)
customers.drop(drop_rows2, axis=0, inplace = True)
customers.shape
Int64Index([ 1, 10, 13, 20, 36, 37, 38, 39,
40, 43,
...
191494, 191527, 191541, 191553, 191565, 191570, 191575, 191597,
191605, 191639],
dtype='int64', length=57406)
Out[0]:
(134246, 237)
In [0]:
# Re-encoding the only binary variable found.
azdias['OST_WEST_KZ'].replace(['W', 'O'], [1, 0], inplace=True)
customers['OST_WEST_KZ'].replace(['W', 'O'], [1, 0], inplace=True)
In [0]:
#dropping columns with greater than 10 different values for simplicity.
cat_cols_to_drop = ['CAMEO_DEU_2015', 'GFK_URLAUBERTYP', 'LP_FAMILIE_FEIN', 'LP_LEBENSPHASE_FEIN',
'LP_LEBENSPHASE_GROB', 'LP_STATUS_FEIN', 'PRAEGENDE_JUGENDJAHRE','CAMEO_DEUG_2015']
# Doing one-hot encoding on columns with less than 10 different values.
dummy_cols = ['CJT_GESAMTTYP', 'FINANZTYP', 'GEBAEUDETYP', 'GEBAEUDETYP_RASTER', 'HEALTH_TYP',
'KBA05_HERSTTEMP', 'KBA05_MAXHERST', 'KBA05_MODTEMP', 'LP_FAMILIE_GROB', 'LP_STATUS_GROB',
'NATIONALITAET_KZ', 'SHOPPER_TYP', 'VERS_TYP']
In [0]:
azdias.drop(columns=cat_cols_to_drop,inplace=True,axis=1)
customers.drop(columns=cat_cols_to_drop,inplace=True,axis=1)
In [0]:
# Create dummy variables for columns with less than 10
# unique values then drop the original columns
def dummies(df):
for col in dummy_cols:
dummy = pd.get_dummies(df[col], prefix = col)
df = pd.concat([df, dummy], axis = 1)
print(df.shape)
df.drop(dummy_cols, axis=1, inplace = True)
print(df.shape)
In [0]:
dummies(azdias)
dummies(customers)
(737288, 292) (737288, 279) (134246, 291) (134246, 278)
In [0]:
# Test to see if columns are alike
for cols in azdias.columns:
if cols not in customers.columns:
print(cols)
In [0]:
# Mean is selected for imputation as after reiterations, it was found to be more useful than median or most frequent.
def impute(df):
imputer = SimpleImputer(missing_values=np.nan,strategy='mean')
return pd.DataFrame(imputer.fit_transform(df), columns = df.columns)
In [0]:
azdias = impute(azdias)
customers = impute(customers)
In [0]:
azdias.shape, customers.shape
Out[0]:
((737288, 229), (134246, 229))
In [0]:
# Test to see if there are still any missing values in azdias
(azdias.shape[0] - azdias.count()).sort_values()
Out[0]:
ANZ_HAUSHALTE_AKTIV 0 KBA13_KW_120 0 KBA13_KW_121 0 KBA13_KW_30 0 KBA13_KW_40 0 KBA13_KW_50 0 KBA13_KW_60 0 KBA13_KW_61_120 0 KBA13_KW_70 0 KBA13_KW_80 0 KBA13_KW_90 0 KBA13_MAZDA 0 KBA13_KW_110 0 KBA13_MERCEDES 0 KBA13_NISSAN 0 KBA13_OPEL 0 KBA13_PEUGEOT 0 KBA13_RENAULT 0 KBA13_SEG_GELAENDEWAGEN 0 KBA13_SEG_GROSSRAUMVANS 0 KBA13_SEG_KLEINST 0 KBA13_SEG_KLEINWAGEN 0 KBA13_SEG_KOMPAKTKLASSE 0 KBA13_SEG_MINIVANS 0 KBA13_SEG_MINIWAGEN 0 KBA13_MOTOR 0 KBA13_KW_0_60 0 KBA13_KRSZUL_NEU 0 KBA13_KRSSEG_VAN 0 KBA13_HALTER_50 0 KBA13_HALTER_55 0 KBA13_HALTER_60 0 KBA13_HALTER_65 0 KBA13_HALTER_66 0 KBA13_HERST_ASIEN 0 KBA13_HERST_AUDI_VW 0 KBA13_HERST_BMW_BENZ 0 KBA13_HERST_EUROPA 0 KBA13_HERST_FORD_OPEL 0 KBA13_HERST_SONST 0 KBA13_KMH_0_140 0 KBA13_KMH_110 0 KBA13_KMH_140 0 KBA13_KMH_140_210 0 KBA13_KMH_180 0 KBA13_KMH_211 0 KBA13_KMH_250 0 KBA13_KMH_251 0 KBA13_KRSAQUOT 0 KBA13_KRSHERST_AUDI_VW 0 KBA13_KRSHERST_BMW_BENZ 0 KBA13_KRSHERST_FORD_OPEL 0 KBA13_KRSSEG_KLEIN 0 KBA13_KRSSEG_OBER 0 KBA13_SEG_MITTELKLASSE 0 KBA13_SEG_OBEREMITTELKLASSE 0 KBA13_SEG_OBERKLASSE 0 KBA13_SEG_SONSTIGE 0 PLZ8_GBZ 0 PLZ8_HHZ 0 REGIOTYP 0 RELAT_AB 0 RETOURTYP_BK_S 0 SEMIO_DOM 0 SEMIO_ERL 0 SEMIO_FAM 0 SEMIO_KAEM 0 SEMIO_KRIT 0 SEMIO_KULT 0 SEMIO_LUST 0 SEMIO_MAT 0 SEMIO_PFLICHT 0 SEMIO_RAT 0 SEMIO_REL 0 SEMIO_SOZ 0 SEMIO_TRADV 0 SEMIO_VERT 0 SHOPPER_TYP 0 VERS_TYP 0 W_KEIT_KIND_HH 0 WOHNDAUER_2008 0 WOHNLAGE 0 ZABEOTYP 0 PLZ8_BAUMAX 0 KBA13_HALTER_45 0 PLZ8_ANTG4 0 PLZ8_ANTG2 0 KBA13_SEG_SPORTWAGEN 0 KBA13_SEG_UTILITIES 0 KBA13_SEG_VAN 0 KBA13_SEG_WOHNMOBILE 0 KBA13_SITZE_4 0 KBA13_SITZE_5 0 KBA13_SITZE_6 0 KBA13_TOYOTA 0 KBA13_VORB_0 0 KBA13_VORB_1 0 KBA13_VORB_1_2 0 KBA13_VORB_2 0 KBA13_VORB_3 0 KBA13_VW 0 KKK 0 KONSUMNAEHE 0 LP_FAMILIE_GROB 0 LP_STATUS_GROB 0 MIN_GEBAEUDEJAHR 0 MOBI_REGIO 0 NATIONALITAET_KZ 0 ONLINE_AFFINITAET 0 ORTSGR_KLS9 0 OST_WEST_KZ 0 PLZ8_ANTG1 0 PLZ8_ANTG3 0 ANREDE_KZ 0 KBA13_HALTER_40 0 KBA13_HALTER_30 0 KBA05_CCM4 0 KBA05_DIESEL 0 KBA05_FRAU 0 KBA05_GBZ 0 KBA05_HERST1 0 KBA05_HERST2 0 KBA05_HERST3 0 KBA05_HERST4 0 KBA05_HERST5 0 KBA05_HERSTTEMP 0 KBA05_KRSAQUOT 0 KBA05_CCM3 0 KBA05_KRSHERST1 0 KBA05_KRSHERST3 0 KBA05_KRSKLEIN 0 KBA05_KRSOBER 0 KBA05_KRSVAN 0 KBA05_KRSZUL 0 KBA05_KW1 0 KBA05_KW2 0 KBA05_KW3 0 KBA05_MAXAH 0 KBA05_MAXBJ 0 KBA05_MAXHERST 0 KBA05_KRSHERST2 0 KBA05_CCM2 0 KBA05_CCM1 0 KBA05_AUTOQUOT 0 ANZ_HH_TITEL 0 ANZ_PERSONEN 0 ANZ_TITEL 0 BALLRAUM 0 CJT_GESAMTTYP 0 EWDICHTE 0 FINANZ_ANLEGER 0 FINANZ_HAUSBAUER 0 FINANZ_MINIMALIST 0 FINANZ_SPARER 0 FINANZ_UNAUFFAELLIGER 0 FINANZ_VORSORGER 0 FINANZTYP 0 GEBAEUDETYP 0 GEBAEUDETYP_RASTER 0 GEBURTSJAHR 0 GREEN_AVANTGARDE 0 HEALTH_TYP 0 HH_EINKOMMEN_SCORE 0 INNENSTADT 0 KBA05_ALTER1 0 KBA05_ALTER2 0 KBA05_ALTER3 0 KBA05_ALTER4 0 KBA05_ANHANG 0 KBA05_MAXSEG 0 KBA05_MOD1 0 KBA05_MOD2 0 KBA05_MOD3 0 KBA13_BJ_1999 0 KBA13_BJ_2000 0 KBA13_BJ_2004 0 KBA13_BJ_2006 0 KBA13_BJ_2008 0 KBA13_BJ_2009 0 KBA13_BMW 0 KBA13_CCM_0_1400 0 KBA13_CCM_1000 0 KBA13_CCM_1200 0 KBA13_CCM_1400 0 KBA13_CCM_1500 0 KBA13_CCM_1600 0 KBA13_CCM_1800 0 KBA13_CCM_2000 0 KBA13_CCM_2500 0 KBA13_CCM_2501 0 KBA13_CCM_3000 0 KBA13_CCM_3001 0 KBA13_FAB_ASIEN 0 KBA13_FAB_SONSTIGE 0 KBA13_FIAT 0 KBA13_FORD 0 KBA13_HALTER_20 0 KBA13_HALTER_25 0 KBA13_AUTOQUOTE 0 KBA13_HALTER_35 0 KBA13_AUDI 0 KBA13_ALTERHALTER_61 0 KBA05_MOD4 0 KBA05_MOD8 0 KBA05_MODTEMP 0 KBA05_MOTOR 0 KBA05_MOTRAD 0 KBA05_SEG1 0 KBA05_SEG10 0 KBA05_SEG2 0 KBA05_SEG3 0 KBA05_SEG4 0 KBA05_SEG5 0 KBA05_SEG6 0 KBA05_SEG7 0 KBA05_SEG8 0 KBA05_SEG9 0 KBA05_VORB0 0 KBA05_VORB1 0 KBA05_VORB2 0 KBA05_ZUL1 0 KBA05_ZUL2 0 KBA05_ZUL3 0 KBA05_ZUL4 0 KBA13_ALTERHALTER_30 0 KBA13_ALTERHALTER_45 0 KBA13_ALTERHALTER_60 0 KBA13_ANZAHL_PKW 0 ALTERSKATEGORIE_GROB 0 dtype: int64
In [0]:
# Test to see if there are still any missing values in customers
(customers.shape[0] - customers.count()).sort_values()
Out[0]:
ANZ_HAUSHALTE_AKTIV 0 KBA13_KW_120 0 KBA13_KW_121 0 KBA13_KW_30 0 KBA13_KW_40 0 KBA13_KW_50 0 KBA13_KW_60 0 KBA13_KW_61_120 0 KBA13_KW_70 0 KBA13_KW_80 0 KBA13_KW_90 0 KBA13_MAZDA 0 KBA13_KW_110 0 KBA13_MERCEDES 0 KBA13_NISSAN 0 KBA13_OPEL 0 KBA13_PEUGEOT 0 KBA13_RENAULT 0 KBA13_SEG_GELAENDEWAGEN 0 KBA13_SEG_GROSSRAUMVANS 0 KBA13_SEG_KLEINST 0 KBA13_SEG_KLEINWAGEN 0 KBA13_SEG_KOMPAKTKLASSE 0 KBA13_SEG_MINIVANS 0 KBA13_SEG_MINIWAGEN 0 KBA13_MOTOR 0 KBA13_KW_0_60 0 KBA13_KRSZUL_NEU 0 KBA13_KRSSEG_VAN 0 KBA13_HALTER_50 0 KBA13_HALTER_55 0 KBA13_HALTER_60 0 KBA13_HALTER_65 0 KBA13_HALTER_66 0 KBA13_HERST_ASIEN 0 KBA13_HERST_AUDI_VW 0 KBA13_HERST_BMW_BENZ 0 KBA13_HERST_EUROPA 0 KBA13_HERST_FORD_OPEL 0 KBA13_HERST_SONST 0 KBA13_KMH_0_140 0 KBA13_KMH_110 0 KBA13_KMH_140 0 KBA13_KMH_140_210 0 KBA13_KMH_180 0 KBA13_KMH_211 0 KBA13_KMH_250 0 KBA13_KMH_251 0 KBA13_KRSAQUOT 0 KBA13_KRSHERST_AUDI_VW 0 KBA13_KRSHERST_BMW_BENZ 0 KBA13_KRSHERST_FORD_OPEL 0 KBA13_KRSSEG_KLEIN 0 KBA13_KRSSEG_OBER 0 KBA13_SEG_MITTELKLASSE 0 KBA13_SEG_OBEREMITTELKLASSE 0 KBA13_SEG_OBERKLASSE 0 KBA13_SEG_SONSTIGE 0 PLZ8_GBZ 0 PLZ8_HHZ 0 REGIOTYP 0 RELAT_AB 0 RETOURTYP_BK_S 0 SEMIO_DOM 0 SEMIO_ERL 0 SEMIO_FAM 0 SEMIO_KAEM 0 SEMIO_KRIT 0 SEMIO_KULT 0 SEMIO_LUST 0 SEMIO_MAT 0 SEMIO_PFLICHT 0 SEMIO_RAT 0 SEMIO_REL 0 SEMIO_SOZ 0 SEMIO_TRADV 0 SEMIO_VERT 0 SHOPPER_TYP 0 VERS_TYP 0 W_KEIT_KIND_HH 0 WOHNDAUER_2008 0 WOHNLAGE 0 ZABEOTYP 0 PLZ8_BAUMAX 0 KBA13_HALTER_45 0 PLZ8_ANTG4 0 PLZ8_ANTG2 0 KBA13_SEG_SPORTWAGEN 0 KBA13_SEG_UTILITIES 0 KBA13_SEG_VAN 0 KBA13_SEG_WOHNMOBILE 0 KBA13_SITZE_4 0 KBA13_SITZE_5 0 KBA13_SITZE_6 0 KBA13_TOYOTA 0 KBA13_VORB_0 0 KBA13_VORB_1 0 KBA13_VORB_1_2 0 KBA13_VORB_2 0 KBA13_VORB_3 0 KBA13_VW 0 KKK 0 KONSUMNAEHE 0 LP_FAMILIE_GROB 0 LP_STATUS_GROB 0 MIN_GEBAEUDEJAHR 0 MOBI_REGIO 0 NATIONALITAET_KZ 0 ONLINE_AFFINITAET 0 ORTSGR_KLS9 0 OST_WEST_KZ 0 PLZ8_ANTG1 0 PLZ8_ANTG3 0 ANREDE_KZ 0 KBA13_HALTER_40 0 KBA13_HALTER_30 0 KBA05_CCM4 0 KBA05_DIESEL 0 KBA05_FRAU 0 KBA05_GBZ 0 KBA05_HERST1 0 KBA05_HERST2 0 KBA05_HERST3 0 KBA05_HERST4 0 KBA05_HERST5 0 KBA05_HERSTTEMP 0 KBA05_KRSAQUOT 0 KBA05_CCM3 0 KBA05_KRSHERST1 0 KBA05_KRSHERST3 0 KBA05_KRSKLEIN 0 KBA05_KRSOBER 0 KBA05_KRSVAN 0 KBA05_KRSZUL 0 KBA05_KW1 0 KBA05_KW2 0 KBA05_KW3 0 KBA05_MAXAH 0 KBA05_MAXBJ 0 KBA05_MAXHERST 0 KBA05_KRSHERST2 0 KBA05_CCM2 0 KBA05_CCM1 0 KBA05_AUTOQUOT 0 ANZ_HH_TITEL 0 ANZ_PERSONEN 0 ANZ_TITEL 0 BALLRAUM 0 CJT_GESAMTTYP 0 EWDICHTE 0 FINANZ_ANLEGER 0 FINANZ_HAUSBAUER 0 FINANZ_MINIMALIST 0 FINANZ_SPARER 0 FINANZ_UNAUFFAELLIGER 0 FINANZ_VORSORGER 0 FINANZTYP 0 GEBAEUDETYP 0 GEBAEUDETYP_RASTER 0 GEBURTSJAHR 0 GREEN_AVANTGARDE 0 HEALTH_TYP 0 HH_EINKOMMEN_SCORE 0 INNENSTADT 0 KBA05_ALTER1 0 KBA05_ALTER2 0 KBA05_ALTER3 0 KBA05_ALTER4 0 KBA05_ANHANG 0 KBA05_MAXSEG 0 KBA05_MOD1 0 KBA05_MOD2 0 KBA05_MOD3 0 KBA13_BJ_1999 0 KBA13_BJ_2000 0 KBA13_BJ_2004 0 KBA13_BJ_2006 0 KBA13_BJ_2008 0 KBA13_BJ_2009 0 KBA13_BMW 0 KBA13_CCM_0_1400 0 KBA13_CCM_1000 0 KBA13_CCM_1200 0 KBA13_CCM_1400 0 KBA13_CCM_1500 0 KBA13_CCM_1600 0 KBA13_CCM_1800 0 KBA13_CCM_2000 0 KBA13_CCM_2500 0 KBA13_CCM_2501 0 KBA13_CCM_3000 0 KBA13_CCM_3001 0 KBA13_FAB_ASIEN 0 KBA13_FAB_SONSTIGE 0 KBA13_FIAT 0 KBA13_FORD 0 KBA13_HALTER_20 0 KBA13_HALTER_25 0 KBA13_AUTOQUOTE 0 KBA13_HALTER_35 0 KBA13_AUDI 0 KBA13_ALTERHALTER_61 0 KBA05_MOD4 0 KBA05_MOD8 0 KBA05_MODTEMP 0 KBA05_MOTOR 0 KBA05_MOTRAD 0 KBA05_SEG1 0 KBA05_SEG10 0 KBA05_SEG2 0 KBA05_SEG3 0 KBA05_SEG4 0 KBA05_SEG5 0 KBA05_SEG6 0 KBA05_SEG7 0 KBA05_SEG8 0 KBA05_SEG9 0 KBA05_VORB0 0 KBA05_VORB1 0 KBA05_VORB2 0 KBA05_ZUL1 0 KBA05_ZUL2 0 KBA05_ZUL3 0 KBA05_ZUL4 0 KBA13_ALTERHALTER_30 0 KBA13_ALTERHALTER_45 0 KBA13_ALTERHALTER_60 0 KBA13_ANZAHL_PKW 0 ALTERSKATEGORIE_GROB 0 dtype: int64
In [0]:
# convert to int for next step
azdias = azdias.astype(int)
customers = customers.astype(int)
In [0]:
# detect and exclude rows with outlier in dataframe using method mentioned in:
# https://stackoverflow.com/questions/23199796/detect-and-exclude-outliers-in-pandas-data-frame
azdias = azdias[(np.abs(stats.zscore(azdias)) < 6).all(axis=1)]
customers = customers[(np.abs(stats.zscore(customers)) < 6).all(axis=1)]
In [0]:
azdias.shape,customers.shape
Out[0]:
((726430, 229), (129213, 229))
In [0]:
# Scaling values for normalization
scaler = StandardScaler()
azdias = pd.DataFrame(scaler.fit_transform(azdias), columns = azdias.columns)
customers = pd.DataFrame(scaler.transform(customers), columns = customers.columns)
In [0]:
customers.head()
Out[0]:
| ANZ_HAUSHALTE_AKTIV | ANZ_HH_TITEL | ANZ_PERSONEN | ANZ_TITEL | BALLRAUM | CJT_GESAMTTYP | EWDICHTE | FINANZ_ANLEGER | FINANZ_HAUSBAUER | FINANZ_MINIMALIST | FINANZ_SPARER | FINANZ_UNAUFFAELLIGER | FINANZ_VORSORGER | FINANZTYP | GEBAEUDETYP | GEBAEUDETYP_RASTER | GEBURTSJAHR | GREEN_AVANTGARDE | HEALTH_TYP | HH_EINKOMMEN_SCORE | INNENSTADT | KBA05_ALTER1 | KBA05_ALTER2 | KBA05_ALTER3 | KBA05_ALTER4 | KBA05_ANHANG | KBA05_AUTOQUOT | KBA05_CCM1 | KBA05_CCM2 | KBA05_CCM3 | KBA05_CCM4 | KBA05_DIESEL | KBA05_FRAU | KBA05_GBZ | KBA05_HERST1 | KBA05_HERST2 | KBA05_HERST3 | KBA05_HERST4 | KBA05_HERST5 | KBA05_HERSTTEMP | ... | LP_FAMILIE_GROB | LP_STATUS_GROB | MIN_GEBAEUDEJAHR | MOBI_REGIO | NATIONALITAET_KZ | ONLINE_AFFINITAET | ORTSGR_KLS9 | OST_WEST_KZ | PLZ8_ANTG1 | PLZ8_ANTG2 | PLZ8_ANTG3 | PLZ8_ANTG4 | PLZ8_BAUMAX | PLZ8_GBZ | PLZ8_HHZ | REGIOTYP | RELAT_AB | RETOURTYP_BK_S | SEMIO_DOM | SEMIO_ERL | SEMIO_FAM | SEMIO_KAEM | SEMIO_KRIT | SEMIO_KULT | SEMIO_LUST | SEMIO_MAT | SEMIO_PFLICHT | SEMIO_RAT | SEMIO_REL | SEMIO_SOZ | SEMIO_TRADV | SEMIO_VERT | SHOPPER_TYP | VERS_TYP | W_KEIT_KIND_HH | WOHNDAUER_2008 | WOHNLAGE | ZABEOTYP | ANREDE_KZ | ALTERSKATEGORIE_GROB | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -0.632714 | -0.1462 | 0.230530 | 0.0 | -0.528379 | 0.984757 | -1.142375 | -1.257692 | -0.849526 | 1.417078 | -1.154003 | -0.436024 | 1.131878 | -0.878295 | -0.681843 | 0.276358 | -1.253842 | 1.868900 | -1.630398 | -2.249555 | -0.270461 | 0.055295 | -0.974471 | 0.933943 | 0.969339 | 0.039948 | 1.661027 | 0.030065 | 0.000033 | -0.024370 | -0.164741 | -0.954019 | 0.938196 | 0.613630 | 1.217323 | 0.012961 | 0.069771 | -0.672625 | -0.621032 | -0.518812 | ... | -0.176514 | 1.700583 | -0.398185 | 0.703120 | -0.346992 | 0.185551 | -1.447435 | 0.514608 | 0.768333 | 0.207562 | -0.612187 | -0.958666 | -0.644619 | 1.467320 | 1.439079 | -1.941597 | -1.538098 | 1.079663 | -1.978441 | -0.885984 | 0.469442 | -1.762217 | -0.869649 | -0.087507 | 1.257354 | 1.116286 | -1.164749 | -1.743572 | -1.049131 | 0.934012 | -1.546271 | 0.910278 | 1.451368 | -1.004050 | 1.076320 | 0.548094 | 1.553998 | -0.279453 | -1.044483 | 1.094113 |
| 1 | -0.632714 | -0.1462 | -0.641977 | 0.0 | 1.305268 | -0.979015 | 0.027728 | -1.257692 | 0.600368 | 1.417078 | -1.154003 | 1.004470 | 1.131878 | -0.878295 | 1.912321 | -0.828924 | -1.253842 | 1.868900 | -0.278796 | -2.249555 | -1.753049 | 0.055295 | 0.907991 | -0.929467 | 0.162682 | 2.154164 | 0.785459 | -0.904345 | -0.961161 | 1.828213 | 0.655212 | 1.875183 | 0.938196 | -0.149103 | 1.217323 | 0.982870 | -1.712837 | -0.672625 | 0.153358 | -0.518812 | ... | -0.755648 | 1.700583 | -0.398185 | -0.003697 | -0.346992 | -1.111776 | -0.133193 | 0.514608 | -0.262151 | 0.207562 | 1.423882 | 0.422115 | 0.736962 | -1.250315 | -0.630007 | 1.432840 | -0.061982 | 1.079663 | 0.226941 | 1.298502 | -1.113209 | 0.909469 | 1.432933 | -1.632361 | 1.257354 | -0.445853 | -0.105717 | -1.139579 | -1.572575 | -1.129545 | -1.546271 | -0.643369 | -0.528158 | 0.995966 | 1.076320 | 0.548094 | -1.083487 | -0.279453 | 0.957411 | 1.094113 |
| 2 | -0.731297 | -0.1462 | -1.514484 | 0.0 | 1.305268 | -0.979015 | -1.727427 | -0.579581 | -0.849526 | 1.417078 | -1.154003 | -1.156271 | 1.131878 | 1.025337 | -0.311248 | 0.276358 | -1.253842 | -0.535074 | -0.278796 | -0.299208 | 1.212127 | 0.055295 | -0.033240 | 0.002238 | 0.162682 | 2.154164 | -0.090108 | 0.030065 | 0.961228 | -0.950662 | -0.984694 | 0.932116 | -0.926813 | 0.613630 | 0.483671 | -1.926858 | 0.069771 | 1.743100 | 0.153358 | 0.394470 | ... | -1.334781 | 1.038503 | -0.398185 | 0.703120 | -0.346992 | -0.463113 | -1.009355 | 0.514608 | 0.768333 | -0.885882 | -0.612187 | -0.958666 | -0.644619 | 0.561442 | -0.630007 | 0.870434 | -1.538098 | -0.292306 | -0.875750 | -0.885984 | 0.469442 | -0.693542 | -0.869649 | -0.087507 | 0.300324 | 0.074860 | -0.635233 | -0.535585 | -0.525687 | 0.934012 | 0.156465 | 1.428161 | -1.517921 | -1.004050 | -0.099781 | 0.548094 | 1.553998 | -1.699860 | -1.044483 | 1.094113 |
| 3 | -0.041213 | -0.1462 | 1.975544 | 0.0 | -0.528379 | 1.639348 | 0.027728 | 0.776640 | -0.849526 | -0.030023 | -1.154003 | 1.724717 | 0.405907 | -0.878295 | 0.059347 | -0.828924 | 0.790124 | -0.535074 | 1.072807 | 1.001023 | -0.270461 | 0.055295 | 0.907991 | 0.933943 | -1.450632 | -1.017159 | -0.090108 | -0.904345 | 1.922422 | -0.950662 | -0.984694 | -0.010952 | 0.005692 | -0.149103 | -0.983632 | 0.982870 | 0.069771 | -0.672625 | 0.153358 | -1.432094 | ... | 1.560885 | -0.947735 | -0.398185 | -0.003697 | -0.346992 | 1.482878 | -0.133193 | 0.514608 | -0.262151 | 1.301006 | 0.405847 | 0.422115 | 0.046172 | -0.344437 | -0.630007 | 1.432840 | -1.538098 | 1.079663 | 0.226941 | -0.339862 | 0.469442 | -1.227879 | -0.869649 | 0.427444 | 0.778839 | 1.116286 | 0.423800 | 0.672401 | -0.002242 | -0.097766 | 0.156465 | 0.392396 | -0.528158 | 0.995966 | -1.275881 | 0.548094 | -0.555990 | -1.699860 | -1.044483 | 0.169218 |
| 4 | -0.632714 | -0.1462 | 0.230530 | 0.0 | 1.305268 | 0.330166 | 0.612779 | -1.257692 | -0.124579 | 1.417078 | -1.154003 | -0.436024 | 1.131878 | 0.549429 | -0.681843 | 0.276358 | -1.253842 | 1.868900 | 1.072807 | -2.249555 | 1.706323 | -1.626044 | -0.974471 | 0.002238 | 1.775995 | -1.017159 | -0.090108 | 0.030065 | -0.961161 | -0.024370 | 2.295118 | -0.954019 | 1.870701 | -0.149103 | 0.483671 | 0.982870 | -1.712837 | 0.132617 | 0.153358 | -0.518812 | ... | -0.176514 | 0.376424 | -0.398185 | -0.003697 | -0.346992 | 0.185551 | 0.742969 | 0.514608 | -0.262151 | 0.207562 | 0.405847 | 0.422115 | -0.644619 | 1.467320 | 1.439079 | -0.816785 | 1.414135 | -0.292306 | 0.226941 | -0.339862 | -0.058108 | -0.693542 | 0.281642 | 0.942396 | 0.778839 | -1.487278 | -0.635233 | -1.139579 | -0.002242 | 0.934012 | 0.156465 | 1.428161 | -0.528158 | 0.995966 | 1.076320 | 0.548094 | -1.610984 | -0.989657 | -1.044483 | 0.169218 |
5 rows × 229 columns
Unsupervised Learning Model¶
In [0]:
pca = PCA().fit(azdias)
plt.figure()
plt.plot(np.cumsum(pca.explained_variance_ratio_))
plt.xlabel('No. of Components')
plt.ylabel('Variance Explained (%)')
plt.show()
In [0]:
def pca_transform(df,n):
global pca
pca = PCA(n_components=n)
transformed_data = pd.DataFrame(pca.fit_transform(df))
print(pca.explained_variance_ratio_.sum())
return transformed_data
In [0]:
# selecting 120 n-components based on above chart as it retains more than 90% variance.
%%time
azdias_pca = pca_transform(azdias, n=120)
customers_pca = pca_transform(customers, n=120)
0.9060771214882293 0.9042891190068887 CPU times: user 2min 1s, sys: 26.7 s, total: 2min 28s Wall time: 47.3 s
In [0]:
def elbow_plot(data, start_K, end_K, step):
'''
Generates an elbow plot to find optimal number of clusters
graphing K values from start_K to end_K every step value
INPUT:
data: Demographics DataFrame
start_K: Inclusive starting value for cluster number
end_K: Exclusive stopping value for cluster number
step: Step value between start_K and end_K
OUTPUT: Trimmed and cleaned demographics DataFrame
'''
score_list = []
for i in range(start_K, end_K, step):
print(i)
start = time.time()
kmeans = KMeans(i)
model = kmeans.fit(data)
score = model.score(data)
score_list.append(abs(score))
end = time.time()
elapsed_time = end - start
print(elapsed_time)
plt.plot(range(start_K, end_K, step),
score_list, linestyle='--', marker='o', color='b');
plt.xlabel('# of clusters K');
plt.ylabel('Sum of squared errors');
plt.title('SSE vs. K');
In [0]:
%%time
elbow_plot(azdias, 1, 20, 2)
1 16.719720125198364 3 113.28463459014893 5 187.56377291679382 7 443.5559284687042 9 435.1332473754883 11 514.429468870163 13 644.1196641921997 15 668.5473103523254 17 761.4800519943237 19 844.9050009250641 CPU times: user 1h 24min 8s, sys: 5min 24s, total: 1h 29min 32s Wall time: 1h 17min 9s
In [0]:
#k=7 is taken as the curve starts to flatten after that
%%time
k_m = KMeans(7)
k_model = k_m.fit(azdias_pca)
k_pred = k_model.predict(azdias_pca)
CPU times: user 3min 35s, sys: 17 s, total: 3min 52s Wall time: 3min 20s
In [0]:
k_pred[:15]
Out[0]:
array([2, 1, 3, 1, 2, 2, 2, 1, 1, 6, 2, 2, 1, 6, 2], dtype=int32)
In [0]:
azdias_clustered = pd.DataFrame(k_model.predict(azdias_pca),columns=['Cluster'])
customers_clustered = pd.DataFrame(k_model.predict(customers_pca),columns=['Cluster'])
In [0]:
azdias_clustered.head()
Out[0]:
| Cluster | |
|---|---|
| 0 | 2 |
| 1 | 1 |
| 2 | 3 |
| 3 | 1 |
| 4 | 2 |
In [0]:
# Finding difference in proportions of customer ratio to population ratio for each cluster.
customer_clusters = customers_clustered['Cluster'].value_counts().sort_index()
population_clusters = azdias_clustered["Cluster"].value_counts().sort_index()
df_stacked = pd.concat([customer_clusters,population_clusters],axis=1).reset_index()
df_stacked.columns = ['Cluster','Customer','Population']
df_stacked['Difference'] = (df_stacked['Customer']/df_stacked['Customer'].sum()*100) - (df_stacked['Population']/df_stacked['Population'].sum()*100)
df_stacked
Out[0]:
| Cluster | Customer | Population | Difference | |
|---|---|---|---|---|
| 0 | 0 | 11007 | 57698 | 0.575814 |
| 1 | 1 | 17691 | 109405 | -1.369292 |
| 2 | 2 | 19172 | 125248 | -2.404062 |
| 3 | 3 | 28823 | 163161 | -0.154085 |
| 4 | 4 | 15731 | 88825 | -0.053135 |
| 5 | 5 | 7389 | 57006 | -2.128953 |
| 6 | 6 | 29400 | 125087 | 5.533713 |
In [0]:
# plotting the ratio values of population and customers per cluster side by side.
df_stacked['Population_Percent'] = (df_stacked['Population']/df_stacked['Population'].sum()*100).round(2)
df_stacked['Customer_Percent'] = (df_stacked['Customer']/df_stacked['Customer'].sum()*100).round(2)
fig = plt.figure()
ax = fig.add_subplot(111)
ax = df_stacked['Population_Percent'].plot(x=df_stacked['Cluster'],width=-0.35,align='edge',color='blue',kind='bar',position=0)
ax = df_stacked['Customer_Percent'].plot(width = 0.35, align='edge',color='orange', kind='bar',position=1)
ax.set_xlabel('Clusters')
ax.set_ylabel('Ratio %')
ax.xaxis.set(ticklabels=range(20))
ax.tick_params(axis = 'x', which = 'major', labelsize = 15)
ax.margins(x=0.5,y=0.1)
plt.legend(('Gen Population', 'Customer'),fontsize=15)
plt.title('Ratio of Population vs Customers as % of total per cluster', fontdict={'fontsize': 22})
plt.show()
In [0]:
# plotting the proportion difference calculated above.
fig = plt.figure()
ax = sns.barplot(x = df_stacked['Cluster'], y = df_stacked['Difference'])
ax.set(title='Difference in proportion between populations',ylabel='Proportion', xlabel='Clusters')
Out[0]:
[Text(0, 0.5, 'Proportion'), Text(0.5, 0, 'Clusters'), Text(0.5, 1.0, 'Difference in proportion between populations')]
In [0]:
target = customers_clustered[customers_clustered['Cluster'] == 6].index
general = azdias_clustered[azdias_clustered['Cluster'] == 2].index
df_target = customers.iloc[target]
df_general = azdias.iloc[general]
df_target_inv = pd.DataFrame(scaler.inverse_transform(df_target),columns=df_target.columns)
df_general_inv = pd.DataFrame(scaler.inverse_transform(df_general),columns=df_general.columns)
In [0]:
df_target_inv.shape
Out[0]:
(29400, 229)
In [0]:
# plotting graph for comparison of each feature on over-represented vs under-represented cluster.
def plot_comparison(column, customer, general, clusters=[6,2]):
'''
Plots the distribution for a feature.
Args:
column (string) - feature to plot.
customer (dataframe) - customer population cluster.
general (dataframe) - general population cluster.
clusters (list) - ints to use when pritting out title.
'''
sns.set(style="darkgrid")
fig, (ax1, ax2) = plt.subplots(figsize=(20,4), ncols=2)
sns.countplot(x=column, data=df_target_inv, ax=ax1, palette="Set2")
ax1.set_xlabel('Value')
ax1.set_title(f'Distribution Customer population (cluster: {clusters[0]})')
sns.countplot(x=column, data=df_general_inv, ax=ax2, palette="Set2")
ax2.set_xlabel('Value')
ax2.set_title(f'Distribution General population (cluster: {clusters[1]})')
fig.suptitle(f'Feature: {column}')
In [0]:
for values in df_target_inv.columns:
plot_comparison(column=values, customer=df_target_inv, general=df_general_inv)
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:12: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). if sys.path[0] == '':
The following conclusion can me made from the graphs above:
Part 2 Supervised Learning Model¶
In [0]:
azdias = pd.read_csv('/content/gdrive/My Drive/Bertelsmann Data Science Project/azdias_with_missing_values.csv')
nans = pd.read_csv('/content/gdrive/My Drive/Bertelsmann Data Science Project/nans.csv')
del nans['Unnamed: 0']
/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py:2718: DtypeWarning: Columns (9) have mixed types. Specify dtype option on import or set low_memory=False. interactivity=interactivity, compiler=compiler, result=result)
In [0]:
mailout_train = pd.read_csv('/content/gdrive/My Drive/Bertelsmann Data Science Project/Train.csv',dtype={18:'str',19:'str'})
mailout_test = pd.read_csv('/content/gdrive/My Drive/Bertelsmann Data Science Project/Test.csv',dtype={18:'str',19:'str'})
In [0]:
mailout_train = mailout_train.set_index('LNR')
mailout_test = mailout_test.set_index('LNR')
In [0]:
y = mailout_train['RESPONSE']
del mailout_train['RESPONSE']
X = mailout_train
In [0]:
plt.title('Distribution of customers responses',fontdict = {'fontsize' : 20})
sns.countplot(y);
Benchmark Model¶
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cols_to_drop = ['CAMEO_DEU_2015', 'CAMEO_DEUG_2015', 'CAMEO_INTL_2015', 'D19_LETZTER_KAUF_BRANCHE','EINGEFUEGT_AM', 'OST_WEST_KZ']
X.drop(columns=cols_to_drop,inplace=True)
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# run simple xgb model with default settings as benchmark
model = xgb.XGBClassifier()
model.fit(X,y)
Out[0]:
XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,
colsample_bynode=1, colsample_bytree=1, gamma=0,
learning_rate=0.1, max_delta_step=0, max_depth=3,
min_child_weight=1, missing=None, n_estimators=100, n_jobs=1,
nthread=None, objective='binary:logistic', random_state=0,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,
silent=None, subsample=1, verbosity=1)
In [0]:
test_pred = model.predict_proba(mailout_test)
In [0]:
submission = pd.DataFrame({'LNR':mailout_test.index, 'RESPONSE':test_pred[:, 1]})
In [0]:
# Submitting to kaggle competitions page gave a score of 0.79772
%cd /content/gdrive/My Drive/Bertelsmann Data Science Project
submission.to_csv('submission_benchmark.csv', index=False)
/content/gdrive/My Drive/Bertelsmann Data Science Project
Data Cleaning¶
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# Making cleaning function for new data sets based on pre-processing done before.
def cleaning_func(df):
for row in nans['Attributes']:
if row in df.columns:
_m = nans.loc[nans['Attributes'] == row, 'missing_or_unknown'].iloc[0]
_i = df.loc[:, row].isin(_m)
df.loc[_i, row] = np.NaN
else:
continue
for col in df.columns:
if df[col].dtype == np.int64:
df[col] = df[col].astype(np.float64)
cols_to_drop = ['CAMEO_DEU_2015', 'CAMEO_DEUG_2015', 'CAMEO_INTL_2015', 'D19_LETZTER_KAUF_BRANCHE','EINGEFUEGT_AM']
df.drop(columns=cols_to_drop,inplace=True)
df['OST_WEST_KZ'].replace(['W', 'O'], [1, 0], inplace=True)
dummy_cols = ['CJT_GESAMTTYP', 'FINANZTYP', 'GEBAEUDETYP', 'GEBAEUDETYP_RASTER', 'HEALTH_TYP',
'KBA05_HERSTTEMP', 'KBA05_MAXHERST', 'KBA05_MODTEMP', 'LP_FAMILIE_GROB', 'LP_STATUS_GROB',
'NATIONALITAET_KZ', 'SHOPPER_TYP', 'VERS_TYP']
for col in dummy_cols:
dummy = pd.get_dummies(df[col], prefix = col)
df = pd.concat([df, dummy], axis = 1)
print(df.shape)
df.drop(dummy_cols, axis=1, inplace = True)
print(df.shape)
imputer = SimpleImputer(missing_values=np.nan,strategy='mean')
df = pd.DataFrame(imputer.fit_transform(df), columns = df.columns,index = df.index)
print(df.head())
# not scaling values for xgboost as it results in better score without it.
# scaler = StandardScaler()
# df = pd.DataFrame(scaler.fit_transform(df), columns = df.columns,index = df.index)
return df
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X_cleaned = cleaning_func(X)
(42962, 422)
(42962, 409)
AGER_TYP AKT_DAT_KL ... VERS_TYP_1.0 VERS_TYP_2.0
LNR ...
1763 2.0 1.0 ... 0.0 1.0
1771 1.0 4.0 ... 1.0 0.0
1776 1.0 1.0 ... 1.0 0.0
1460 2.0 1.0 ... 0.0 1.0
1783 2.0 1.0 ... 1.0 0.0
[5 rows x 409 columns]
In [0]:
mailout_test_cleaned = cleaning_func(mailout_test)
(42833, 422)
(42833, 409)
AGER_TYP AKT_DAT_KL ... VERS_TYP_1.0 VERS_TYP_2.0
LNR ...
1754 2.000000 1.0 ... 1.0 0.0
1770 1.715618 1.0 ... 1.0 0.0
1465 2.000000 9.0 ... 1.0 0.0
1470 1.715618 7.0 ... 0.0 1.0
1478 1.000000 1.0 ... 1.0 0.0
[5 rows x 409 columns]
Model Selection¶
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# Choosing the algorithm with best base performance.
def fit_classifier(model, params, X=X_cleaned, y=y):
"""
Fits a classifier to its training data using GridSearchCV and calculates ROC AUC score
INPUT:
- clf (classifier): classifier to fit
- params (dict): classifier parameters used with GridSearchCV
- X_train (DataFrame): training input
- y_train (DataFrame): training output
OUTPUT:
- classifier: input classifier fitted to the training data
"""
# cv uses StratifiedKFold
start = time.time()
grid = GridSearchCV(estimator=model, param_grid=params, scoring='roc_auc', cv=5, verbose=0)
print("Training {} :".format(model.__class__.__name__))
grid.fit(X, y)
end = time.time()
time_taken = round(end-start,2)
print(model.__class__.__name__)
print("Time taken : {} secs".format(time_taken))
print("Best score : {}".format(round(grid.best_score_,4)))
print("*"*40)
return grid.best_score_, grid.best_estimator_, time_taken
In [0]:
abc = AdaBoostClassifier(random_state=42) # AdaBoostClassifier
gbc = GradientBoostingClassifier(random_state=42) # GradientBoostingClassifier
xgbc = xgb.XGBClassifier(random_state=42) # XGBoost Classifier
cbc = CatBoostClassifier(random_state=42) # CatBoost Classifier
lgbm = lgb.LGBMClassifier(random_state=42) # Light GMB
In [0]:
model_names = []
model_scores = []
model_best_ests = []
model_time_taken = []
model_dict = {}
for model in [abc, gbc,xgbc,cbc,lgbm]:
best_score, best_est, time_taken = fit_classifier(model, {})
model_names.append(model.__class__.__name__)
model_scores.append(best_score)
model_best_ests.append(best_est)
model_time_taken.append(time_taken)
Training AdaBoostClassifier : AdaBoostClassifier Time taken : 94.31 secs Best score : 0.7259 **************************************** Training GradientBoostingClassifier : GradientBoostingClassifier Time taken : 405.97 secs Best score : 0.7531 **************************************** Training XGBClassifier : XGBClassifier Time taken : 186.93 secs Best score : 0.7574 **************************************** Training CatBoostClassifier : Learning rate set to 0.046653 0: learn: 0.5984964 total: 118ms remaining: 1m 57s 1: learn: 0.5174848 total: 192ms remaining: 1m 35s 2: learn: 0.4491613 total: 255ms remaining: 1m 24s 3: learn: 0.3909536 total: 327ms remaining: 1m 21s 4: learn: 0.3425278 total: 384ms remaining: 1m 16s 5: learn: 0.3016333 total: 461ms remaining: 1m 16s 6: learn: 0.2667780 total: 539ms remaining: 1m 16s 7: learn: 0.2371073 total: 610ms remaining: 1m 15s 8: learn: 0.2127636 total: 674ms remaining: 1m 14s 9: learn: 0.1918296 total: 747ms remaining: 1m 13s 10: learn: 0.1736684 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Hyperparameter Optimzation¶
In [0]:
# intermediate model
# The XGBoost model gave the best base score therefore it was tuned to beat the benchmark score.
# submitting model to kaggle. Multiple parameters were trained using some help from the following links.
# https://towardsdatascience.com/fine-tuning-xgboost-in-python-like-a-boss-b4543ed8b1e
%%time
# Due to technical limitations, I ultimately ended up using randomizedsearch instead of gridsearch here.
model = xgb.XGBClassifier(objective= 'binary:logistic')
param_grid = {'subsample' : [0.8,1],
'n_estimators' : [100, 1000],
'learning_rate' : [0.01,0.001],
'max_depth': [4,5,6,7],
'scale_pos_weight': [10,25],
'gamma': [1,5,10],
'colsample_bytree' : [0.3,0.5,0.8]
}
kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)
random_search = RandomizedSearchCV(model,param_distributions=param_grid,n_iter=50,scoring='roc_auc',n_jobs=-1,cv=kfold,verbose=1)
#grid_search = GridSearchCV(model, param_grid = param_grid, scoring="roc_auc", n_jobs=-1, cv=kfold, verbose=1)
grid_result = random_search.fit(X_cleaned, y)
print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))
Fitting 5 folds for each of 50 candidates, totalling 250 fits
[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers. [Parallel(n_jobs=-1)]: Done 42 tasks | elapsed: 46.6min /usr/local/lib/python3.6/dist-packages/joblib/externals/loky/process_executor.py:706: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak. "timeout or by a memory leak.", UserWarning [Parallel(n_jobs=-1)]: Done 192 tasks | elapsed: 175.5min [Parallel(n_jobs=-1)]: Done 250 out of 250 | elapsed: 242.1min finished
Best: 0.766841 using {'subsample': 0.8, 'scale_pos_weight': 10, 'n_estimators': 100, 'max_depth': 7, 'learning_rate': 0.01, 'gamma': 5, 'colsample_bytree': 0.8}
CPU times: user 1min 13s, sys: 710 ms, total: 1min 14s
Wall time: 4h 3min 14s
In [0]:
# final model
# The XGBoost model gave the best base score therefore it was tuned to beat the benchmark score.
# submitting model to kaggle. Multiple parameters were trained using some help from the following links.
# https://towardsdatascience.com/fine-tuning-xgboost-in-python-like-a-boss-b4543ed8b1e
%%time
model = xgb.XGBClassifier(objective= 'binary:logistic')
param_grid = {'n_estimators' : [20, 50, 100],
'learning_rate' : [0.01,0.001],
'max_depth': [4,5,6,7],
'scale_pos_weight': [1,10],
'colsample_bytree' : [0.5,0.8]
}
kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)
grid_search = GridSearchCV(model, param_grid = param_grid, scoring="roc_auc", n_jobs=-1, cv=kfold, verbose=1)
grid_result = grid_search.fit(X_cleaned, y)
print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))
Fitting 5 folds for each of 96 candidates, totalling 480 fits
[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers. [Parallel(n_jobs=-1)]: Done 42 tasks | elapsed: 2.9min [Parallel(n_jobs=-1)]: Done 192 tasks | elapsed: 15.8min [Parallel(n_jobs=-1)]: Done 442 tasks | elapsed: 45.0min [Parallel(n_jobs=-1)]: Done 480 out of 480 | elapsed: 51.1min finished
Best: 0.766173 using {'colsample_bytree': 0.5, 'learning_rate': 0.01, 'max_depth': 5, 'n_estimators': 100, 'scale_pos_weight': 10}
CPU times: user 31.5 s, sys: 705 ms, total: 32.2 s
Wall time: 51min 33s
In [0]:
# submitted after the model above but gave worse score.
# The XGBoost model gave the best base score therefore it was tuned to beat the benchmark score.
# submitting model to kaggle. Multiple parameters were trained using some help from the following links.
# https://towardsdatascience.com/fine-tuning-xgboost-in-python-like-a-boss-b4543ed8b1e
# this time with 10 splits.
%%time
model = xgb.XGBClassifier(objective= 'binary:logistic')
param_grid = {'n_estimators' : [20, 50, 100],
'learning_rate' : [0.01,0.001],
'max_depth': [4,5,6,7],
'scale_pos_weight': [1,10],
'colsample_bytree' : [0.5,0.8]
}
kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=42)
grid_search = GridSearchCV(model, param_grid = param_grid, scoring="roc_auc", n_jobs=-1, cv=kfold, verbose=1)
grid_result = grid_search.fit(X_cleaned, y)
print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))
Fitting 10 folds for each of 96 candidates, totalling 960 fits
[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers. [Parallel(n_jobs=-1)]: Done 42 tasks | elapsed: 2.6min [Parallel(n_jobs=-1)]: Done 192 tasks | elapsed: 16.9min [Parallel(n_jobs=-1)]: Done 442 tasks | elapsed: 40.1min [Parallel(n_jobs=-1)]: Done 792 tasks | elapsed: 89.0min [Parallel(n_jobs=-1)]: Done 960 out of 960 | elapsed: 115.1min finished
Best: 0.770858 using {'colsample_bytree': 0.8, 'learning_rate': 0.01, 'max_depth': 5, 'n_estimators': 100, 'scale_pos_weight': 10}
CPU times: user 47.6 s, sys: 2.03 s, total: 49.7 s
Wall time: 1h 55min 46s
Part 3: Kaggle Competition¶
In [0]:
## best params
opt_params = {'colsample_bytree': 0.5, 'learning_rate': 0.01, 'max_depth': 5, 'n_estimators': 100, 'scale_pos_weight': 10}
model = xgb.XGBClassifier(**opt_params)
model.fit(X_cleaned,y)
Out[0]:
XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,
colsample_bynode=1, colsample_bytree=0.5, gamma=0,
learning_rate=0.01, max_delta_step=0, max_depth=5,
min_child_weight=1, missing=None, n_estimators=100, n_jobs=1,
nthread=None, objective='binary:logistic', random_state=0,
reg_alpha=0, reg_lambda=1, scale_pos_weight=10, seed=None,
silent=None, subsample=1, verbosity=1)
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test_pred = model.predict_proba(mailout_test_cleaned)
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submission = pd.DataFrame({'LNR':mailout_test_cleaned.index, 'RESPONSE':test_pred[:, 1]})
In [0]:
%cd /content/gdrive/My Drive/Bertelsmann Data Science Project
submission.to_csv('submission_again.csv', index=False)
/content/gdrive/My Drive/Bertelsmann Data Science Project